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Publishing type：Research paper (international conference proceedings)   Publisher：Elsevier  

DOI： 10.1016/j.procs.2021.08.133

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Other Link： https://dblp.uni-trier.de/db/conf/kes/kes2021.html#DaoKI21

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/AIKE48582.2020.00034

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Other Link： https://dblp.uni-trier.de/db/conf/aike/aike2020.html#NararatwongKI20

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS  

This paper introduces an automatic semantic annotation system, namely MTab4Wikidata, for the three semantic annotation tasks, i.e., Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), Column Relation-Property Annotation (CPA), of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020). In particular, we introduce (1) a novel fuzzy entity search to address misspelling table cells, (2) a fuzzy statement search to deal with ambiguous cells, (3) a statement enrichment module to address the Wikidata shifting issue, (4) an efficient and effective post-processing for the matching tasks. Our system achieves impressive empirical performance for the three annotation tasks and wins the first prize at SemTab 2020. MTab4Wikidata is ranked 1st in the two tasks of CEA and CPA, and 2nd rank in the CTA task on the round 1, 2, 3 datasets and 1st rank on the round 4 dataset and the Tough Tables (2T) dataset.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Science and Business Media Deutschland GmbH  

Traditional exploration methods in reinforcement learning rely on well-designed extrinsic rewards. However, many real-world scenarios involve sparse or delayed rewards. One solution inspired by curious behaviors in animals is to let the agent develop its own intrinsic rewards. In this paper we propose a novel end-to-end curiosity mechanism which uses learning progress as novelty bonus. We compare a policy-based and a visual-based progress bonus to move the agent towards hard-to-learn regions of the state space. We further leverage the agent’s learning to identify the most critical regions, which results in more sample-efficient and global exploration strategies. We evaluate our method on a variety of benchmark environments, including Minigrid, Super Mario Bros., and Atari games. Experimental results show that our method outperforms prior approaches in most tasks in terms of exploration efficiency and average scores, especially for those featuring high-level exploration patterns or with deceptive rewards.

DOI： 10.1007/978-3-030-61616-8_22

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：International Joint Conferences on Artificial Intelligence  

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent's ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS  

SeMantic AnsweR Type prediction (SMART) challenge proposed a task to determine the types of answers given natural language questions. Understanding answer types play a crucial role in question answering. In this paper, we present Hierarchical Contextualized-based models, namely HiCoRe, for the SAMRT task. HiCoRe builds on top of state of the art contextualized-based models and the hierarchical strategy to deal with the hierarchical answer types. The SMART results show that HiCoRe obtains promising performance for answer type prediction on DBpedia and Wikidata datasets.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Science and Business Media Deutschland GmbH  

Knowledge Graph (KG) is a popular way of storing facts about the real world entities, where nodes represent the entities and edges denote relations. KG is being used in many AI applications, so several large scale Knowledge Graphs (KGs) e.g., DBpedia, Wikidata, YAGO have become extremely popular. Unfortunately, very limited number of the entities stored in different KGs are aligned. This paper presents an embedding-based entity alignment method. Existing methods mainly focus on the relational structures and attributes to align the same entities of two different KGs. Such methods fail when the entities have less number of attributes or when the relational structure may not capture the meaningful representation of the entities. To solve this problem, we propose a Joint Summary and Attribute Embeddings (JSAE) based entity alignment method. We exploit the entity summary information available in KGs for entities’ summary embedding. To learn the semantics of the entity summary we employ Bidirectional Encoder Representations from Transformers (BERT). Our model learns the representations of entities by using relational triples, attribute triples and description as well. We perform experiments on real-world datasets and the results indicate that the proposed approach significantly outperforms the state-of-the-art models for entity alignment.

DOI： 10.1007/978-3-030-61705-9_10

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Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

DOI： 10.1109/jproc.2018.2837045

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Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS.org  

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Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s00354-019-00076-w

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer New York LLC  

Temporal features, such as an explicit date and time or a time-specific event, employ concise semantics for any kind of information retrieval. Therefore, temporal features should be suitable for linked data information retrieval. However, we have found that most linked data information retrieval techniques pay little attention to the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework ‘ that can incorporate temporal features and give more concise results. The evaluation of our system performance indicates that it is promising.

DOI： 10.1007/s10844-017-0483-2

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery  

Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning episodes are required which may be too time consuming or even impossible especially in real-world scenarios. We present a new architecture to combine external knowledge and deep reinforcement learning using only visual input. A key concept of our system is augmenting image input by adding environment feature information and combining two sources of decision. We evaluate the performances of our method in a 3D partially-observable environment from the Microsoft Malmo platform. Experimental evaluation exhibits higher performance and faster learning compared to a single reinforcement learning model.

DOI： 10.1145/3167132.3167165

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：AAAI press  

Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidate triples. TransE is the first translation-based method and it is well known because of its simplicity and efficiency for knowledge graph completion. It employs the principle that the differences between entity embeddings represent their relations. The principle seems very simple, but it can effectively capture the rules of a knowledge graph. However, TransE has a problem with its regularization. TransE forces entity embeddings to be on a sphere in the embedding vector space. This regularization warps the embeddings and makes it difficult for them to fulfill the abovementioned principle. The regularization also affects adversely the accuracies of the link predictions. On the other hand, regularization is important because entity embeddings diverge by negative sampling without it. This paper proposes a novel embedding model, TorusE, to solve the regularization problem. The principle of TransE can be defined on any Lie group. A torus, which is one of the compact Lie groups, can be chosen for the embedding space to avoid regularization. To the best of our knowledge, TorusE is the first model that embeds objects on other than a real or complex vector space, and this paper is the first to formally discuss the problem of regularization of TransE. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult and ComplEx on a standard link prediction task. We show that TorusE is scalable to large-size knowledge graphs and is faster than the original TransE.

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Language：English   Publishing type：Part of collection (book)   Publisher：IGI Global  

The goal of linked data instance matching is to detect all instances that co-refer to the same objects in two linked data repositories, the source and the target. Since the amount of linked data is rapidly growing, it is important to automate this task. However, the difference between the schemata of source and target repositories remains a challenging barrier. This barrier reduces the portability, accuracy, and scalability of many proposed approaches. The authors present automatic schema-independent interlinking (ASL), which is a schema-independent system that performs instance matching on repositories with different schemata, without prior knowledge about the schemata. The key improvements of ASL compared to previous systems are the detection of useful attribute pairs for comparing instances, an attribute-driven token-based blocking scheme, and an effective modification of existing string similarities. To verify the performance of ASL, the authors conducted experiments on a large dataset containing 246 subsets with different schemata. The results show that ASL obtains high accuracy and significantly improves the quality of discovered coreferences against recently proposed complex systems.

DOI： 10.4018/978-1-5225-5191-1.ch065

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electronics, Information and Communication, Engineers, IEICE  

Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.

DOI： 10.1587/transinf.2017SWP0006

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Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

Online services make users’ communication activities and content public. This online information has contributed to accelerating the creation cycle of user-generated content. Moreover, these services also allow researchers to utilize these online texts as a public source for easily analyzing human activities, also referred to as social sensing studies. However, we need to realize that there exists a controversial problem of privacy especially in the sensitive areas of creation, even though the content is public. This study tries to create new guidelines for online study using the case of the flaming of a study of female fan-fiction, which attempted to extract and filter sexual expressions using online fan-fiction novels as source. Researchers from the fields of both engineering and humanities, including law and ethics, discussed the violations in this case, and extracted ethical, legal, and social issues according to their specific areas of expertise.

DOI： 10.11517/pjsai.jsai2018.0_3h2os25b04

CiNii Research

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

The increased penetration of distributed and volatile renewable generation requires the demand-side to be actively involved in energy balancing operations. This paper proposes a solution in which big data and machine learning methods are employed to enhance the capabilities of a Virtual Power Plant to participate and intelligently bid into a demand response energy market. The energy market being investigated consists of the frequency containment reserve market. First, we define the core decision-making required to overcome the uncertainties in the frequency containment reserve market participation for a Virtual Power Plant. Then, we focus on forecasting the frequency containment reserve prices for the day-ahead. We analyze the price data, and identify and collect the relevant features for the prediction of the prices. In addition, we select several regression analysis methods to be utilized for the prediction. Finally, we evaluate the performance of the implemented methods by executing several experiments, and compare the the performance with the performance of a state of the art autoregression method.

DOI： 10.1109/IECON.2017.8217316

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

Due to the rise of artificial intelligence (AI) technology, discussions are progressing on how robots could replace human labor. Conventional surveys have suggested that human labor is expected to gradually be replaced as tasks become automated. We conducted a survey at the world's first robot hotel recently opened in Japan-called a Henn-na hotel (strange/change hotel) in Japanese-which already uses robots for most of the work. We discovered that human labor is divided into small tasks, and that robot actions affect human emotional control. However, the hotel not only divides human work but also reconstructs it from tasks. Moreover, the purpose of reconstruction is not simply for replacement of works. Such modification of task is often observed taking place in humansystem interactions. It is an extremely creative process of labor emerging in this area.

DOI： 10.1109/ROMAN.2017.8172305

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Knowledge graphs have been shown to be useful to many tasks in artificial intelligence. Triples of knowledge graphs are traditionally structured by human editors or extracted from semi-structured information; however, editing is expensive, and semi-structured information is not common. On the other hand, most such information is stored as text. Hence, it is necessary to develop a method that can extract knowledge from texts and then construct or populate a knowledge graph; this has been attempted in various ways. Currently, there are two approaches to constructing a knowledge graph. One is open information extraction (Open IE), and the other is knowledge graph embedding; however, neither is without problems. Stanford Open IE, the current best such system, requires labeled sentences as training data, and knowledge graph embedding systems require numerous triples. Recently, distributed representations of words have become a hot topic in the field of natural language processing, since this approach does not require labeled data for training. These require only plain text, but Mikolov showed that it can perform well with the word analogy task, answering questions such as, "a is to b as c is to __?." This can be considered as a knowledge extraction task from a text for finding the missing entity of a triple. However, the accuracy is not sufficiently high when applied in a straightforward manner to relations in knowledge graphs, since the method uses only one triple as a positive example. In this paper, we analyze why distributed representations perform such tasks well; we also propose a new method for extracting knowledge from texts that requires much less annotated data. Experiments show that the proposed method achieves considerable improvement compared with the baseline; in particular, the improvement in HITS@10 was more than doubled for some relations.

DOI： 10.1587/transinf.2017EDP7112

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

DOI： 10.1587/transinf.2016EDP7337

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

Instance matching is the finding of co-referent instances that describe the same real-world object across two different repositories. For this problem, the heterogeneity, also known as the differences of objects' attributes and repositories' schema, is a challenging issue. It creates the limitations in the accuracy of existing solutions. In order to match the instances of heterogeneous repositories, a matching system can follow a configuration that specifies the equivalent properties, suitable similarity metrics, and other important parameters. This configuration can be created manually or automatically by learning methods. We present ScLink, an instance matching system that can generate a configuration automatically. In ScLink, we install two novel supervised learning algorithms, cLearn and minBlock. cLearn applies an apriori-like heuristic for finding the optimal combination of matching properties and similarity metrics. minBlock finds a blocking model, which aims at optimally reducing the pairwise alignments of instances between input repositories. In addition, ScLink introduces other techniques to take into account the scalability issue on large repositories. Experimental results on standard and very large datasets find that minBlock and cLearn are very effective and efficient. cLearn is also significantly better than existing configuration learning algorithms. It drastically boosts the accuracy of ScLink and makes the system outperform the state-of-the-arts, even when being trained using a small amount of labeled data.

DOI： 10.1007/s10844-016-0426-3

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

Due to the rise of artificial intelligence (AI) technology, discussions are progressing on how robots could replace human labor. Conventional surveys have shown that human work is expected to gradually be replaced as tasks become automated. However, human labor changes on tasks, not on jobs. The modification is taking place in human-system interactions. There is an extremely creative process of labor emerging in this area. We conducted a survey at the world's first robot hotel - also called a Henn-na hotel ("strange/change hotel") in Japanese - which already uses robots for most of the work done there. We discovered that human work is divided into task units, and that robot actions affect human emotional control.

DOI： 10.1145/3029798.3038312

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IGI GLOBAL  

The goal of linked data instance matching is to detect all instances that co-refer to the same objects in two linked data repositories, the source and the target. Since the amount of linked data is rapidly growing, it is important to automate this task. However, the difference between the schemata of source and target repositories remains a challenging barrier. This barrier reduces the portability, accuracy, and scalability of many proposed approaches. The authors present automatic schema-independent interlinking (ASL), which is a schema-independent system that performs instance matching on repositories with different schemata, without prior knowledge about the schemata. The key improvements of ASL compared to previous systems are the detection of useful attribute pairs for comparing instances, an attribute-driven token-based blocking scheme, and an effective modification of existing string similarities. To verify the performance of ASL, the authors conducted experiments on a large dataset containing 246 subsets with different schemata. The results show that ASL obtains high accuracy and significantly improves the quality of discovered coreferences against recently proposed complex systems.

DOI： 10.4018/IJSWIS.2017010106

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SCITEPRESS  

Continuous representations language models have gained popularity in many NLP tasks. To measure the similarity of two words, we have to calculate their cosine distances. However the qualities of word embeddings depend on the corpus selected. As for word2vec, we observe that the vectors are far apart to each other. Furthermore, synonym words with low occurrences or with multiple meanings are even further in distance. In these cases, cosine similarities are no longer appropriate to evaluate how similar the words are. And considering about the structures of most of the language models, they are not as deep as we supposed. "Deep" here refers to setting more layers in the neural network. Based on these observations, we implement a mixed system with two kinds of architectures. We show that adjustment can be done on word embeddings in both unsupervised and supervised ways. Remarkably, this approach can successfully handle the cases mentioned above by largely increasing most of synonyms similarities. It is also easy to train and adapt to certain tasks by changing the training target and dataset.

DOI： 10.5220/0006120003980406

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Relations play a vital role on knowledge construction and maintenance thereof. They for example connect domain type entities to range type entities, like the relation born in connects some Persons to some Places. Over any dataset, the domain-range information is used to maintain data consistency. Therefore, we see that knowledge construction frameworks sometime engage costly Knowledge Engineers to define the domain-range information in form of a schema or an ontology. We also see that frameworks that hold such defined domain range information, often do not follow them strictly. In the worst case some frameworks do not even allow to define a domain-range, rather they just gather the knowledge entries. One reason of not defining the domain-range information is that it is costly. On the other hand, the reason for not following the domain-range constraint is that the most of them are either manual or semi-automatic, therefore they face adaptation difficulty. In this research, we propose a relation-wise machine learning model that can define and validate domain-range information automatically. The initial experiment shows that the proposed framework performs promisingly.

DOI： 10.1109/ICSC.2017.70

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

A coreference refers to different instances of the same real-world entity. Coreference classification is an important problem in knowledge and data management. The basic idea is to predict whether two instances are matched or non-matched, based on their similarity vector. Previous efforts on coreference classification share a common weakness. It is the unawareness of the ambiguity variation among different clusters of instance-pairs. We discuss the importance of cluster wise instance-pairs local ranking, which is an effective solution for the ambiguity variation. Since then, we study the inclusive possibility of the ranking factor in a classifier globally trained from all clusters. Finally, we propose to include an extra element in the original similarity vector of the instance pairs. Such extra element is a ranking-aware feature, which represents the preference of an instance-pair against its cluster. The experiment results confirm that the proposed feature significantly boosts the performance of many classifiers.

DOI： 10.1109/ICSC.2017.106

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

DOI： 10.1109/MTS.2016.2618719

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Mapping instances to the Linked Open Data (LOD) cloud plays an important role for enriching information of instances, since the LOD cloud contains abundant amounts of interlinked instances describing the instances. Consequently, many techniques have been introduced for mapping instances to a LOD data set; however, most of them merely focus on tackling with the problem of heterogeneity. Unfortunately, the problem of the large number of LOD data sets has yet to be addressed. Owing to the number of LOD data sets, mapping an instance to a LOD data set is not sufficient because an identical instance might not exist in that data set. In this article, we therefore introduce a heuristic expansion based framework for mapping instances to LOD data sets. The key idea of the framework is to gradually expand the search space from one data set to another data set in order to discover identical instances. In experiments, the framework could successfully map instances to the LOD data sets by increasing the coverage to 90.36%. Experimental results also indicate that the heuristic function in the framework could efficiently limit the expansion space to a reasonable space. Based upon the limited expansion space, the framework could effectively reduce the number of candidate pairs to 9.73% of the baseline without affecting any performances.

DOI： 10.1587/transinf.2015EDP7390

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Linked data entity resolution is the detection of instances that reside in different repositories but co-describe the same topic. The quality of the resolution result depends on the appropriateness of the configuration, including the selected matching properties and the similarity measures. Because such configuration details are currently set differently across domains and repositories, a general resolution approach for every repository is necessary. In this paper, we present c Link, a system that can perform entity resolution on any input effectively by using a learning algorithm to find the optimal configuration. Experiments show that c Link achieves high performance even when being given only a small amount of training data. c Link also outperforms recent systems, including the ones that use the supervised learning approach.

DOI： 10.1587/transinf.2015EDP7392

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

These days, the Web contains a huge volume of (semi-) structured data, called Linked Data (LD). However, LD suffer in data quality, and this poor data quality brings the need to identify erroneous data. Because manual erroneous data checking is impractical, automatic erroneous data detection is necessary. According to the data publishing guidelines of LD, data should use (already defined) ontology which populates type-annotated LD. Usually, the data type annotation helps in understanding the data. However, in our observation, the data type annotation could be used to identify erroneous data. Therefore, to automatically identify possible erroneous data over the type-annotated LD, we propose a framework that uses a novel nearest-neighbor based error detection technique. We conduct experiments of our framework on DBpedia, a type-annotated LD dataset, and found that our framework shows better performance of error detection in comparison with state-of-the-art framework.

DOI： 10.1587/transinf.2015DAP0022

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.

DOI： 10.1007/s10489-015-0657-y

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER INT PUBLISHING AG  

Whole brain architecture (WBA) which uses neural networks to imitate a human brain is attracting increased attention as a promising way to achieve artificial general intelligence, and distributed vector representations of words is becoming recognized as the best way to connect neural networks and knowledge. Distributed representations of words have played a wide range of roles in natural language processing, and they have become increasingly important because of their ability to capture a large amount of syntactic and lexical meanings or relationships. Relation vectors are used to represent relations between words, but this approach has some problems; some relations cannot be easily defined, for example, sibling relations, parent-child relations, and many-to-one relations. To deal with these problems, we have created a novel way of representing relations: we represent relations by planes instead of by vectors, and this increases by more than 10 % the accuracy of predicting the relation.

DOI： 10.1007/978-3-319-46687-3_33

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ELSEVIER SCIENCE BV  

There are many cognitive architectures available nowadays, and each architecture has its own different mechanisms. Therefore, we need to identify the advantages and disadvantages of these architectures in order to improve upon them. In this paper, we propose new metrics for comparing cognitive architectures based on the Cattell-Horn-Carroll (CHC) model, which is used in psychology to explain factors of intelligence. Here, we analyze factors of intelligence in the CHC model and interpret them as elements of a new cognitive architecture. Then, the CHC model is investigated with respect to "data" and "processing" to obtain a metric for each component. We present examples using Soar and LIDA to illustrate comparing different cognitive architectures and demonstrate the effectiveness of our approach.

DOI： 10.1016/j.procs.2016.07.431

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DOI： 10.1241/johokanri.59.322

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Making fast driving decisions at intersections is a challenging problem for improving safety of autonomous vehicles. Furthermore, representing sensor data in a machine understandable format is essential to enable vehicles to understand traffic situations. Ontologies are used to represent knowledge of sensor data for autonomous vehicles to aware traffic situations. In this paper, we introduce a fast decision making system, which utilizes only related part of the ontology-based knowledge base to make decisions at intersections. The decision making system performs real-time reasoning using traffic regulations and a part of the map information from the knowledge base.

DOI： 10.1109/IVS.2016.7535382

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

Content Words (CWs) are important segments of the text. In text mining, we utilize them for various purposes such as topic identification, document summarization, question answering etc. Usually, the identification of CWs requires various language dependent tools. However, such tools are not available for many languages and developing of them for all languages is costly. On the other hand, because of recent growth of text contents in various languages, language independent text mining carries great potentiality. To mine text automatically, the language tool independent CWs finding is a requirement. In this research, we devise a framework that identifies text segments into CWs in a language independent way. We identify some structural features that relate text segments into CWs. We devise the features over a large text corpus and apply machine learning-based classification that classifies the segments into CWs. The proposed framework only uses large text corpus and some training examples, apart from these, it does not require any language specific tool. We conduct experiments of our framework for three different languages: English, Vietnamese and Indonesian, and found that it works with more than 83% accuracy.

DOI： 10.1007/978-3-319-32034-2_4

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

Instance matching in linked data has become increasingly important because of the rapid development of linked data. The goal of instance matching is to detect co-referent instances that refer to the same real-world objects. In order to realize such instances, instance matching systems use a configuration, which specifies the matching properties, similarity measures, and other settings of the matching process. For different repositories, the configuration is varied to adapt with the particular characteristics of the input. Therefore, the automation of configuration creation is very important. In this paper, we propose cLink, a supervised instance matching system for linked data. cLink is enhanced by a heuristic algorithm that learns the optimal configuration on the basic of input repositories. We show that cLink can achieve effective performance even when being given only a small amount of training data. Compared to previous configuration learning algorithms, our algorithm significantly improves the results. Compared to the recent supervised systems, cLink is also consistently better on all tested datasets.

DOI： 10.1007/978-3-319-31676-5_4

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DOI： 10.1527/tjsai.30_639

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DOI： 10.1145/2740908.2741697

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Since it is difficult to understand or predict research trends, we proposed methodologies for understanding and predicting research trends in the sciences, focusing on the structures of grants in the Japan Society for the Promotion of Science (JSPS), a Japanese funding agency. Grant applications are suitable for predicting research trends because these are research plans for the future, different from papers, which report research outcomes in the past. We investigated research trends in science focusing on research histories identified in grant application data of JSPS. Then we proposed a model for predicting research trends, assuming that breakthrough research encourages researchers to change from their current research field to an entirely new research field. Using breakthrough research, we aim to obtain higher precision in the prediction results. In our experimental results, we found that research fields in Informatics correlate well with actual scientific research trends. We also demonstrated that our prediction models are effective in actively interacting research areas, which include Informatics and Social Sciences.

DOI： 10.1587/transinf.2013EDP7435

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

TheWeb is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

DOI： 10.1007/978-3-319-17966-7_24

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

Information access over linked data requires to determine subgraph(s), in linked data's underlying graph, that correspond to the required information need. Usually, an information access framework is able to retrieve richer information by checking of a large number of possible subgraphs. However, on the fly checking of a large number of possible subgraphs increases information access complexity. This makes an information access frameworks less effective. A large number of contemporary linked data information access frameworks reduce the complexity by introducing different heuristics but they suffer on retrieving richer information. Or, some frameworks do not care about the complexity. However, a practically usable framework should retrieve richer information with lower complexity. In linked data information access, we hypothesize that pre-processed data statistics of linked data can be used to efficiently check a large number of possible subgraphs. This will help to retrieve comparatively richer information with lower data access complexity. Preliminary evaluation of our proposed hypothesis shows promising performance.

DOI： 10.1007/978-3-319-15615-6_12

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.

DOI： 10.1109/IVS.2015.7225667

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

Intelligent Speed Adaptation (ISA) is one of the key technologies for Advanced Driver Assistance Systems (ADAS), which aims to reduce car accidents by supporting drivers to comply with the speed limit. Context awareness is indispensable for autonomous cars to perceive driving environment, where the information should be represented in a machine-understandable format. Ontologies can represent knowledge in a format that machines can understand and perform human-like reasoning. In this paper, we present an ontology-based ISA system that can detect overspeed situations by accessing to the ontology-based Knowledge Base (KB). We conducted experiments on a car simulator as well as on real-world data collected with an intelligent car. Sensor data are converted into RDF stream data and we construct SPARQL queries and a C-SPARQL query to access to the Knowledge Base. Experimental results show that the ISA system can promptly detect overspeed situations by accessing to the ontology-based Knowledge Base.

DOI： 10.1007/978-3-319-15615-6_30

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

The amount and variety of social media content such as status, images, movies, and music are increasing rapidly. Accordingly, the social curation service is emerging as a new way to connect, select, and organize information on a massive scale. One noticeable feature of social curation services is that they are loosely supervised: the content that users create in the service is manually collected, selected, and maintained. A large proportion of these contents are arbitrarily created by inexperienced users. In this paper, we look into social curation, particularly, the Storify website1. This is the most popular social curation for creating stories included in various domains such as Twitter, Flicker, and YouTube.We propose a machine learning method with feature extraction to filter these contents and to predict the popularity of social curation data.

DOI： 10.1007/978-3-319-11680-8_33

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Berlin Heidelberg  

The Linked Open Data cloud contains tremendous amounts of interlinked instances with abundant knowledge for retrieval. However, because the ontologies are large and heterogeneous, it is time-consuming to learn all the ontologies manually and it is difficult to learn the properties important for describing instances of a specific class. To construct an ontology that helps users to easily access various data sets, we propose a semi-automatic system, called the Framework for InTegrating Ontologies, that can reduce the heterogeneity of the ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based approach for finding the core ontology classes and properties, and integrated ontology constructor. By analyzing the instances of linked data sets, this framework constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.

DOI： 10.1007/s13740-014-0041-9

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Keyword-based linked data information retrieval is an easy choice for general-purpose users, but the implementation of such an approach is a challenge because mere keywords do not hold semantic information. Some studies have incorporated templates in an effort to bridge this gap, but most such approaches have proven ineffective because of inefficient template management. Because linked data can be presented in a structured format, we can assume that the data's internal statistics can be used to effectively influence template management. In this work, we explore the use of this influence for template creation, ranking, and scaling. Then, we demonstrate how our proposal for automatic linked data information retrieval can be used alongside familiar keyword-based information retrieval methods, and can also be incorporated alongside other techniques, such as ontology inclusion and sophisticated matching, in order to achieve increased levels of performance.

DOI： 10.1587/transinf.2014EDP7073

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets. © 2014 Springer International Publishing Switzerland.

DOI： 10.1007/978-3-319-07467-2_12

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers. The minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion. Based on our method, each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers. Experimental results show that our technique gives significantly higher performance compared to traditional methods including One-Versus-All, the dense random code, and the sparse random code. Moreover, our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to One-Versus-One.

DOI： 10.1109/IJCNN.2014.6889436

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

This paper presents a semi-automatic similarity aggregating system for ontology matching problem. The system consists of two main parts. The first part is aggregation of similarity measures with the help of self-organizing map. The second part incorporates user feedback for refining self-organizing map outcomes. The system calculates different similarity measures (e. g., string-based similarity measure, WordNet-based similarity measure...) to cover different causes of semantic heterogeneity. The next step is similarity aggregation by means of the self-organizing map and the ward clustering. The final step is the active learning phase for results tuning. We implemented this idea as MAPSOM framework. Our experimental results show that MAPSOM framework can be used for problems where the highest precision is needed.

DOI： 10.1007/978-3-319-06826-8_26

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

Temporal features, such as date and time or time of an event, employ concise semantics for any kind of information retrieval, and therefore for linked data information retrieval. However, we have found that most linked data information retrieval techniques pay little attention on the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework, called TLDRet, that can incorporate temporal features and give more concise results. Preliminary evaluation of our system shows promising performance.

DOI： 10.1007/978-3-319-06826-8_18

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

This paper presents the first framework for integrating procedural knowledge, or "know-how", into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS  

The goal of instance matching is to detect identity resources, which refer to the same real-world object. In this paper, we introduce SLINT+, a novel interlinking system. SLINT+ detects all identity linked data resources between two given repositories. SLINT+ does not require the specifications of RDF predicates and labeled matching resources. SLINT+ performs competitively at OAEI instance matching campaign this year.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focused on predicting links, in social networks using information flow via active links. The information flow heavily depends on link activeness. The links become active if the interactions happen frequently and recently with respect to the current time. The time stamps of the interactions or links provide vital information for determining the activeness of the links. In the present paper, we introduced a new algorithm, referred to as T_Flow, that captures the important aspects of information flow via active links in social networks. We tested T_Flow with two social network data sets, namely, a data set extracted from Facebook friendship network and a coauthorship network data set extracted from ePrint archives. We compare the link prediction performances of T_Flow with the previous method Prop Flow. The results of T_Flow method revealed a notable improvement in link prediction for facebook data and significant improvement in link prediction for coauthorship data.

DOI： 10.1587/transinf.E96.D.1495

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

The Linking Open Data (LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 31 billion RDF triples. Accessing linked data is a challenging task because each data set in the LOD cloud has a specific ontology schema, and familiarity with the ontology schema used is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experiments show that our Mid-Ontology learning approach successfully integrates diverse ontology schema with a high quality, and effectively retrieves related information with the constructed Mid-Ontology.

DOI： 10.1587/transinf.E96.D.40

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

The explosion of linked data is creating sparse connection networks, primarily because more and more missing links among difference data sources are resulting from asynchronous and independent database development. DHR was proposed in other research to discover these links. However, DHR has limitations in a distributed environment. For example, while deploying on a distributed SPARQL server, the data transfer usually causes overhead on the network. Therefore, we propose a new method of detecting a missing link based on DHR. The method consists of two stages: finding the frequent graph and matching the similarity. In this paper, we enhance some features in the two stages to reduce the data flow before querying. We conduct an experiment using geographic data sources with a large number of triples to discover the missing links and compare the accuracy of our proposed matching method with DHR and the primitive mix similarity method. The experimental results show that our method can reduce a large amount of data flow on a network and increase the accuracy of discovering missing links.

DOI： 10.1007/978-3-642-36543-0_48

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Language：English   Publishing type：Research paper (international conference proceedings)  

Linked data interlinking is the discovery of every owl:sameAs links between given data sources. An owl:sameAs link declares the homogeneous relation between two instances that co-refer to the same real-world object. Traditional methods compare two instances by pre-defined pairs of RDF predicates, and therefore they rely on the domain of the data. Recently, researchers have attempted to achieve the domain-independent goal by automatically building the linkage rules. However they still require the human curation for the labeled data as the input for learning process. In this paper, we present SLINT+, an interlinking system that is training-free and domain-independent. SLINT+ finds the important predicates of each data sources and combines them to form predicate alignments. The most useful alignments are then selected in the consideration of their confidence. Finally, SLINT+ uses selected predicate alignments as the guide for generating candidate and matching instances. Experimental results show that our system is very efficient when interlinking data sources in 119 different domains. The very considerable improvements on both precision and recall against recent systems are also reported. © Springer-Verlag 2013.

DOI： 10.1007/978-3-642-37996-3_8

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Language：English   Publishing type：Research paper (international conference proceedings)  

Template-based information access, in which templates are constructed for keywords, is a recent development of linked data information retrieval. However, most such approaches suffer from ineffective template management. Because linked data has a structured data representation, we assume the data's inside statistics can effectively influence template management. In this work, we use this influence for template creation, template ranking, and scaling. Our proposal can effectively be used for automatic linked data information retrieval and can be incorporated with other techniques such as ontology inclusion and sophisticated matching to further improve performance. © Springer-Verlag 2013.

DOI： 10.1007/978-3-642-37996-3_12

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

DOI： 10.1587/transinf.E95.D.821

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DOI： 10.18919/jkg.62.12_514

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Because almost all linked data sources are currently published by different providers, interlinking homogeneous in- stances of these sources is an important problem in data integration. Recently, instance matching has been used to identify owl:sameAs links between linked datasets. Previous approaches primarily use predefined maps of correspond- ing attributes and most of them are limited to matching in specific domains. In this paper, we propose the LFM, a learning-based instant matching system, which is designed for achieving a reliable domain-independent matcher. First, we compute the similarity vectors between labeled pairs of instances without specifying the meaning of the RDF pred- icates. Then a learning process is applied to learn a tree classifier for predicting whether the new pairs of instances are identical. Experiments demonstrate that our method achieves a 4% improvement in precision and recall against recent top-ranked matchers, if we use a small amount of labeled data for learning. Copyright © 2012 ACM.

DOI： 10.1145/2350190.2350197

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Language：English   Publishing type：Research paper (international conference proceedings)  

The Linking Open Data(LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 26 billion RDF triples. Consuming linked data is a challenging task because each data set in the LOD cloud has specific ontology schema, and familiarity with ontology schema is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experimental results show that our Mid-Ontology learning approach successfully integrates diverse ontology schema, and effectively retrieves related information. © 2012 Springer-Verlag.

DOI： 10.1007/978-3-642-29923-0_8

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Language：English   Publishing type：Research paper (international conference proceedings)  

Linked data interlinking is the discovery of all instances that represent the same real-world object and locate in different data sources. Since different data publishers frequently use different schemas for storing resources, we aim at developing a schema-independent interlinking system. Our system automatically selects important predicates and useful predicate alignments, which are used as the key for blocking and instance matching. The key distinction of our system is the use of weighted co-occurrence and adaptive filtering in blocking and instance matching. Experimental results show that the system highly improves the precision and recall over some recent ones. The performance of the system and the.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.

DOI： 10.1587/transinf.E94.D.1419

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DOI： 10.1007/978-3-642-23544-3_26

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Although there have been many recent studies of link prediction in co-authorship networks, few have tried to utilize the Semantic information hidden in abstracts of the research documents. We propose to build a link predictor in a co-authorship network where nodes represent researchers and links represent co-authorship. In this method, we use the structure of the constructed graph, and propose to add a semantic approach using abstract information, research titles and the event information to improve the accuracy of the predictor. Secondly, we make use of the fact that researchers tend to work in close knit communities. The knowledge of a pair of researchers lying in the same dense community can be used to improve the accuracy of our predictor further. Finally, we test out hypothesis on the DBLP database in a reasonable time by under-sampling and balancing the dataset using decision trees and the SMOTE technique. © 2010 IEEE.

DOI： 10.1109/ICMLC.2010.25

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DOI： 10.1007/978-3-642-17313-4_39

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Most of the data on the Web is stored in relational databases. In order to make the Semantic Web grow we need to provide easy-to-use tools to convert those databases into linked data, so that even people with little knowledge of the semantic web can use them. Some programs able to convert relational databases into RDF files have been developed, but the user still has to link manually the database attribute names to existing ontology properties and this generated "linked data" is not actually linked with external relevant data. We propose here a method to associate automatically attribute names to existing ontology entities in order to complete the automation of the conversion of databases. We also present a way - rather basic, but with low error rate - to add links automatically to relevant data from other data sets.

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Language：English   Publishing type：Research paper (international conference proceedings)  

This paper presents an aggregation approach of similarity measures for ontology matching called n-Harmony. The n-Harmony measure identifies top-n highest values in each similarity matrix to assign a weight to the corresponding similarity measure for aggregation. We can also exclude noisy similarity measures that have a low weight and the n-Harmony outperforms previous methods in our experimental tests.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IARIA XPS PRESS  

The amount of linked data is growing rapidly, and so finding suitable entities to link together requires greater effort. For small data sets, it is easy enough to find entities in the data sources and link these together manually; however, doing so for large data sets is impractical. For large sets, a way is needed to discover entities and connect them automatically. In this paper, we present an algorithm to detect hidden owl: sameAs links or hidden relations in data sets. Since geographic names are often highly ambiguous, we used data sets comprising geographic names to implement and evaluate our algorithm. We experimentally compare our algorithm with a naive algorithm that only uses a URI's name feature. We found that it is more accurate than the naive algorithm in most cases, especially for resources in which there is little matching information about features.

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DOI： 10.1007/978-3-642-13025-0_56

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

A supergraph containment search is to retrieve the data graphs contained by a query graph. In this paper, we study the problem of efficiently retrieving all data graphs approximately contained by a query graph, namely similarity search on supergraph containment. We propose a novel and efficient index to boost the efficiency of query processing. We have studied the query processing cost and propose two index construction strategies aimed at optimizing the performance of different types of data graphs: top-down strategy and bottom-up strategy. Moreover, a novel indexing technique is proposed by effectively merging the indexes of individual data graphs; this not only reduces the index size but also further reduces the query processing time. We conduct extensive experiments on real data sets to demonstrate the efficiency and the effectiveness of our techniques.

DOI： 10.1109/ICDE.2010.5447846

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DOI： 10.14864/fss.25.0.139.0

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

This paper presents an analysis of similarity measures for identifying ontology mapping. Using discriminant analysis, we investigated forty-eight similarity measures such as string matching and knowledge based similarities that have been used in previous systems. As a result, we extracted twenty-two effective similarity measures for identifying ontology mapping out of forty-eight possible similarity measures. The extracted measures vary widely in the type in similarity.

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DOI： 10.1007/978-3-540-89447-6_7

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DOI： 10.11517/pjsai.JSAI08.0.211.0

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Information Science and Technology Association, Japan  

The progress of information and communication technologies has enabled us to obtain much more information than before. The huge volume of information needs to be categorized by computer in order for us to use it efficiently.Categorization is also useful for computers, i.e., for intelligent information processing. In this paper, we discuss basic techniques for devising concepts for categorization by computer. The techniques include formal concept analysis to enumerate concepts for categorization, clustering to construct concepts, and catalog integration to integrate categorizations with different concept hierarchies.

DOI： 10.18919/jkg.58.2_78

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In this study, we proposed the use of grant application data to acquire knowledge of the relationships among scientific research subjects. We modeled grant application data to construct a method of capturing the relationships among research subjects, then conducted experiments using actual grant application data. The results indicated that our method successfully elucidated the relationships among research subjects. © 2008 IEEE.

DOI： 10.1109/IV.2008.45

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

This paper presents a new framework for the ontology mapping problem. We organized the ontology mapping problem into a standard machine learning framework, which uses multiple concept similarity measures. We presented several concept similarity measures for the machine learning framework and conducted experiments for testing the framework using real-world data. Our experimental results show that our approach has increased performance with respect to precision, recall and F-measure in comparison with other methods.

DOI： 10.1109/ICIS.2008.51

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

In this study, we proposed the use of grant application data to acquire knowledge of the relationships among scientific research subjects. We modeled grant application data to construct a method of capturing the relationships among research subjects, then conducted experiments using actual grant application data. The results indicated that our method successfully elucidated the relationships among research subjects.

DOI： 10.1109/IV.2008.45

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DOI： 10.1109/IV.2007.26

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DOI： 10.1109/IV.2007.95

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As information technology progress, we are able to obtain much information about the advanced research of others. As a result, researchers and research managers need to track the current research trends amid the information flood. In order to support these efforts to gather knowledge of current research, we propose a research trend mining method. The method utilizes an author-topic model for establishing the relationships between authors, topics, and papers by probabilities, and interactively visualizes the relationships using self-organizing maps. We implemented a research area mapping system and validated it with a case study. In addition, we conducted experiments to show the performance of our system. The experimental results indicate that this system can induce the appropriate relationships for finding research trends. © 2007 IEEE.

DOI： 10.1109/IV.2007.95

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/IV.2006.92

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Language：Japanese  

CiNii Books

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.224.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.242.0

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.61.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.97.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.258.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.60.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.66.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI06.0.223.0

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

Since research trends can change dynamically, researchers have to keep up with new research trends and undertake new research topics. Therefore, research communities for new research domains are important. In this paper, we propose a method to discover research communities. The key feature of our method is a network model of papers and a word assignment technique for the communities obtained. We show the performance of the proposed method using experiments with real world data.

Scopus

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Language：Japanese  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

The research community plays a very important role in helping researchers undertake new research topics. The authors propose a community mining system that helps to find communities of researchers by using bibliography data. The basic concept of this system is to provide interactive visualization of communities both local and global communities. We implemented this concept using actual bibliography data and present a case study using the proposed system.

DOI： 10.1007/11780496_48

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

The research community plays a very important role in holding valuable scientific knowledge. The authors propose a community mining system which helps to find communities of researchers by using bibliography data. The key features of our method are a network model of papers and a word assignment technique for the communities obtained. We implemented the proposed method in a graphical computer system. In this paper, we show how research communities are found using our system. Also, we evaluate the performance of the proposed method using experiments with real world data. The results demonstrate that our system can find appropriately sized research communities for a particular scientific field.

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Language：English   Publishing type：Research paper (scientific journal)  

This paper addresses the problem of learning control skills from observation. In particular, we show how to infer a hierarchical, reactive program that reproduces and explains the observed actions of other agents, specifically the elements that are shared across multiple individuals. We infer these programs using a three-stage process that learns flat unordered rules, combines these rules into a classification hierarchy, and finally translates this structure into a hierarchical reactive program. The resulting program is concise and easy to understand. This makes it possible to view program induction as a practical technique for knowledge acquisition. © 2005 Wiley Periodicals, Inc.

DOI： 10.1002/scj.20312

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI05.0.8.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI05.0.43.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI05.0.130.0

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

Research communities are very important for researchers undertaking new research topics. In this paper, we propose a community mining system using bibliography data in order to find communities of researchers. The basic concept of our study is to provide interactive visualization of both local and global research communities. We implement this concept using actual bibliography data and present a case study using the proposed system.

DOI： 10.1109/IV.2005.35

Web of Science

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Language：English   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.1527/tjsai.19.521

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.1527/tjsai.19.389

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI04.0.58.0

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI04.0.208.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI04.0.33.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI04.0.253.0

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本データベース学会  

CiNii Books

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. This paper proposes a new method of integrating two catalogs with hierarchical categories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. We conducted experiments using two actual Internet directories, and the results show improved performance compared with the previous approach.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

In this paper, we discuss importance and usefulness of inter-personal network in a community support system. We built a scheduling support system for an academic conference. Our system supports information exchange among participants and information discovery with generating participants' interpersonal network. This system was used in an academic conference called JSAI2003 involving 276 active users. The analysis of the networks reveals that interpersonal networks can promote information exchange among people by indicating existence of people to the others, and that it can also support information discovery by recommendation. © Springer-Verlag Berlin Heidelberg 2007.

DOI： 10.1007/978-3-540-71009-7_20

Scopus

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

Hierarchical categorization is a powerful and convenient method so that it is commonly used in various areas, such as ontologies. Although each hierarchy is useful, there are problems to manage multiple hierarchies. In this paper, we propose an alignment method between concept hierarchies by using the similarity of the categorization and the contents of the instance. By using this method, instances that exist in one hierarchy system but does not in the other can be located in a suitable position in the other. The experimental results show improved performance compared with the previous approaches.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. The Yahoo! Internet directory is one such example. This paper proposes a new method of integrating two catalogs with hierarchical categories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show improved performance compared with the previous approaches.

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Language：English   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hypothesis. In order to evaluate the proposed method, we conducted experiments using real-world medical data.

Scopus

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI03.0.47.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI03.0.239.0

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

DOI： 10.1527/tjsai.17.230

CiNii Books

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Other Link： https://jlc.jst.go.jp/DN/JALC/00151428555?from=CiNii

Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

This paper addresses the problem of learning control skills from observation. In particular, we show how to infer a hierarchical, reactive program that reproduces and explains the observed actions of other agents, specifically the elements that are shared across multiple individuals. We infer these programs using a three-stage process that learns flat unordered rules, combines these rules into a classification hierarchy, and finally translates this structure into a hierarchical reactive program. The resulting program is concise and easy to understand, making it possible to view program induction as a practical technique for knowledge acquisition.

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)  

Over the past decade, several inductive logic programming (ILP) systems have been developed. However, the normal ILP system does not have enough power to induce logic programs in some domains. Therefore, various new ILP systems, such as nonmonotonic ILP, that apply new learning tasks, have been proposed. In the present paper, a new learning task, called relational learning from nondichotomizable examples, is proposed for relational domains. The scheme of this task is similar to that of normal ILP, with the exception of the training examples. Normally, conventional ILP uses positive and negative examples in the training process. However, the examples of the proposed learning task are not strictly positive or negative: the training examples have a continuous scale. This new learning task is defined and a new method for learning this task is proposed. A new ILP system, SYNGIP (for SYNthesized system using Genetic programming and Inductive logic Programming), is developed based on this method and the performance of SYNGIP is compared experimentally to that of the conventional ILP method for the task of human feeling acquisition. © 2001 Scripta Technica, Syst. Comp. Jpn.

DOI： 10.1002/scj.1090

Scopus

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Language：English   Publishing type：Research paper (bulletin of university, research institution)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

DOI： 10.11517/pjsai.JSAI01.0.51.0

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Language：English  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：JOHN WILEY & SONS LTD  

Two approaches to inducing a concept represented in first-order logic are Inductive Logic Programming (ILP) and Genetic Programming (GP). These two approaches are very similar with respect to method and goal, yet no previous work has combined ILP and GP. In the present paper, we propose a method that synthesizes the Inductive Logic Programming and Genetic Programming approaches. The main concept behind this approach is to combine the search method of GP with the type and mode methods of ILP. Experimental results show that the proposed method can be used to treat-learning from both positive and negative training examples and learning from training examples that do not have discrete class. Moreover, the proposed method constitutes a novel solution to the closure problem and provides a new bias in concept learning.

Web of Science

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Book type：Scholarly book

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Language：English   Publisher：Information Processing Society of Japan (IPSJ)  

In mining time series data, the graph similarity of the data can be used as an effective tool. However, when the time series has missing data, the utility of graph similarity in analyzing time series data is limited. The present study investigates experimentally the impact of missing values in time series data on dynamic time warping, a method that is commonly used in determining graph similarity. Based on the results of the investigation, we propose a new method by which to treat time series data having missing values. The proposed method uses point similarity rather than graph similarity. Experiments were conducted in order to evaluate the performance of the proposed method, and the results indicate that the proposed method is effective for finding features in time series data.

CiNii Books

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Other Link： http://id.nii.ac.jp/1001/00050304/

Language：English  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

CiNii Books

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Language：English   Publisher：人工知能学会  

CiNii Books

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Language：Japanese   Publisher：人工知能学会  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Knowledge discovery in databases (KDD) requires huge data, which takes a long time to be preprocessed. Although each element of pre-processing is simple, it tends to be quite complicated and is hard to construct the whole plan. To reduce the load, we propose the original model for pre-processing and an interactive and dynamic planning tool for pre-processing, named TransX. This system is based on XML transformation, which enables to visualize the process by using a treelike notation and it allows a user to process data easily and understandably. We propose the original model for preprocessing scheme, which enables to define the preprocessing plan simply, and shows how the system, TransX, carries out the plan concretely

CiNii Books

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