Takahiro Kawamura

Shusaku Egami, Takanori Ugai, Masateru Oota et al.

Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>). Knowledge graph embedding (KGE) is crucial in machine learning applications, specifically in node classification and link prediction tasks. KGE remains a vital research topic within the semantic web community. RDF-star introduces the concept of a quoted triple (QT), a specific form of triple employed either as the subject or object within another triple. Moreover, RDF-star permits a QT to act as compositional entities within another QT, thereby enabling the representation of recursive, hyper-relational KGs with nested structures. However, existing KGE models fail to adequately learn the semantics of QTs and entities, primarily because they do not account for RDF-star graphs containing multi-leveled nested QTs and QT-QT relationships. This study introduces RDF-star2Vec, a novel KGE model specifically designed for RDF-star graphs. RDF-star2Vec introduces graph walk techniques that enable probabilistic transitions between a QT and its compositional entities. Feature vectors for QTs, entities, and relations are derived from generated sequences through the structured skip-gram model. Additionally, we provide a dataset and a benchmarking framework for data mining tasks focused on complex RDF-star graphs. Evaluative experiments demonstrated that RDF-star2Vec yielded superior performance compared to recent extensions of RDF2Vec in various tasks including classification, clustering, entity relatedness, and QT similarity.

Takanori Ugai, Shusaku Egami, Swe Nwe Nwe Htun et al.

This paper presents a synthetic multimodal dataset of daily activities that fuses video data from a 3D virtual space simulator with knowledge graphs depicting the spatiotemporal context of the activities. The dataset is developed for the Knowledge Graph Reasoning Challenge for Social Issues (KGRC4SI), which focuses on identifying and addressing hazardous situations in the home environment. The dataset is available to the public as a valuable resource for researchers and practitioners developing innovative solutions recognizing human behaviors to enhance safety and well-being in

Takahiro Kawamura, Shusaku Egami, Koutarou Tamura et al.

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is becoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which characters are criminals while providing a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story. This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the second prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi-agents model. We conclude this paper with the plans and issues for the next challenge in 2019.

Keisuke Umezu, Takahiro Kawamura, Akihiko Ohsuga

Aging society has been becoming a global problem not only in advanced countries. Under such circumstances, it is said that participation of elderly people in social activities is highly desirable from various perspectives including decrease of social welfare costs. Thus, we propose a mobile service that notifies barrier information nearby users outside to lowers the anxiety of elderly people and promote their social activities. There are barrier free maps in some areas, but those are static and updated annually at the earliest. However, there exist temporary barriers like road repairing and parked bicycles, and also every barrier is not for every elder person. That is, the elder people are under several conditions and wills to go out, so that a barrier for an elder person is not necessarily the one for the other. Therefore, we first collect the barrier information in the user participatory manner and select the ones the user need to know, then timely provide them via a mobile phone equipped with GPS. This paper shows the public experiment that we conducted in Tokyo, and confirms the usability and the accuracy of the information filtering.