Knowledge graphs (KGs) are powerful tools for representing and reasoning over structured information. Their main components include schema, identity, and context. While schema and identity matching are well-established in ontology and entity matching research, context matching remains largely unexplored. This is particularly important because real-world KGs often vary significantly in source, size, and information density—factors not typically represented in the datasets on which current entity matching methods are evaluated. As a result, existing approaches may fall short in scenarios where diverse and complex contexts need to be integrated. To address this gap, we propose a novel KG integration method consisting of label matching and triple matching. We use string manipulation, fuzzy matching, and vector similarity techniques to align entity and predicate labels. Next, we identify mappings between triples that convey comparable information, using these mappings to improve entity-matching accuracy. Our approach demonstrates competitive performance compared with leading systems in the OAEI competition and against supervised methods, achieving high accuracy across diverse test cases. Additionally, we introduce a new dataset derived from the benchmark dataset to evaluate the triple-matching step more comprehensively.

This paper presents an outline of our research project developed at our research center for "Juris-Informatics". "Juris-Informatics" is a research field based on two main topics; "Law by AI" and "Law of AI". "Law by Ai" is a research field where we investigate a support tool by AI for legal activities such as legal reasoning and legal document processing. "Law of AI" is a research field where we conduct research on legal control of AI such as considering the legal responsibility of AI and legal compliance of AI.

Wikidata is the largest collaborative general knowledge graph supported by a worldwide community. It includes many helpful topics for knowledge exploration and data science applications. However, due to the enormous size of Wikidata, it is challenging to retrieve a large amount of data with millions of results, make complex queries requiring large aggregation operations, or access too many statement references. This paper introduces our preliminary works on Wikidata-lite, a toolkit to build a database offline for knowledge extraction and exploration, e.g., retrieving item information, statements, provenances, or searching entities by their keywords, attributes. Wikidata-lite has high performance and memory efficiency, much faster than the official Wikidata SPARQL endpoint for big queries. The Wikidata-lite repository is available at https://github.com/phucty/wikidb.

Thanks to the open government initiative movement, many base registries have been made available and beneficial for all citizens to ensure accountability and transparency. All data are easy to access and process by people for effective, efficient public oversight. However, most base registries publish their data in a different data model or structure. As a result, building an AI system to utilize these data is challenging since they are machine-unreadable, poorly managed, and scattered. This paper proposes a design method for base registry data structures that uses unification and federation to improve the accessibility and availability of data. We also use Wikibase as an underlying knowledge graph to inherit common sense knowledge available in Wikidata to improve its usability.

Wikipedia is the user contributed encyclopedia edited collaboratively by a wide-range of people. Wikipedia usually determines contents of article and editorial policies through discussion among participants. It requires a lot of effort in deducing its conclusion often due to protracted discussion. We need some measurement to examine how discussion is valuable to reach conclusion. So, we call it discussion validity and define it with the model with discussion capability of participants. Discussion capability of participants consists of three features each of which represents characteristic aspect of users' behavior in discussion, and approximated from the corresponding three features of their utterances. We conducted the experiments with the subjects to verify the model and found that our model resulted better than the conventional model and proposed the automatic prediction of discussion validity using the text analysis. Then we estimated discussion validity through the model with the estimated values. It turned out that the estimation was well fitted with the values by the subjects.

User-generated content can help the growth of linked data. However, we lack interfaces enabling ordinary people to author linked data. Secondly, people have multiple perspectives on the same concept and different contexts. Thirdly, not enough ontologies exist to model various data. Therefore, we propose an approach to enable people to share various data through an easy-to-use social platform. Users define their own concepts and multiple conceptualizations are allowed. These are consolidated using semi-automatic schema alignment techniques supported by the community. Further, concepts are grouped semi-automatically by similarity. As a result of consolidation and grouping, informal lightweight ontologies emerge gradually. We have implemented social software, called StYLiD, to realize our approach. It can serve as a platform motivating people to bookmark and share different things. It may also drive vertical portals for specific communities with integrated data from multiple sources. Experimental observations support the validity of our approach.

Recently, designers have been required solving comprehensive problem becoming greater and more complicated. In relation to this background, we have proposed Universal Abduction Studio (UAS); a computer environment that synthetically supports creative design. However, it is difficult for a designer to manually acquire multiple domain knowledge required for UAS. Therefore, we propose a Web-based knowledge database construction method for supporting design by UAS in this paper.

A social network can become bases for information infrastructure in the future. It is important to extract social networks that are not biased. Providing a simple means for users to register their social relation is also important. We propose a method that combines various approaches to extract social networks. Especially, three kinds of networks are extracted; user-registered Know link network, Web-mined Web link network, and face-to-face Touch link network. In this paper, the combination of social network extraction for communities is described, and the analysis on the extracted social networks is shown.

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.

We propose a collaborative approach for personal task management which is modeled as an integration of alliance and human-in-the-loop model. Alliance model is based on information sharing and collaboration of several persons. They disclose their task condition and maintain to be updatable by their friends. To avoid privacy issues we propose emergent group discovery algorithm to control the level of disclosure. Human-in-the-loop model consists of three subsystems to support decision-making activities. Visualizer indicates the attributes associated with each task such as the deadline, the subjective priority, and the workload, which are determined by the user. Optimizer generates executable schedules from these tasks by active scheduler and multiobjective genetic algorithm. Recommender evaluates these alternatives by analytic hierarchy process. We implement client/server system called social scheduler on cell-phones environment. We remark the advantages of our approach with an experiment.

In this paper, we discuss how ontology plays roles in building a distributed and heterogeneous knowledge-base system. First, we discuss relationship between ontology and agents in the [Formula: see text] which is a framework of knowledge sharing and reuse based on a multi-agent architecture. Ontology is a minimum requirement for each agent to join the [Formula: see text]. Second, we explain mediation by ontology to show how ontology is used in the [Formula: see text]. A special agent called mediator analyzes undirected messages and infer candidates of recipient agents by consulting ontology and relationship between ontology and agents. Third, we model ontology as combination of aspects each of which can represent a way of conceptualization. Aspects are combined either as combination aspect which means integration of aspects or category aspect which means choice of aspects. Since ontology by aspect allows heterogeneous and multiple descriptions for phenomenon in the world, it is appropriate for heterogeneous knowledge-base systems. We also show translation of messages as a way of interpreting multiple aspects. A translation agent can translate a message with some aspect to one with another aspect by analyzing dependency of aspects. Mediation and translation of messages are important to build agents easily and naturally because less knowledge on other agents is requested for each agent.