Data-knowledge Integration Research Team
Team Outline
AI technology is rapidly progressing into society and the people's every-day life, ranging from child-care, care-services and safety management to well-being, healthy living, and active aging. It is thus important that AI can be used to realize solutions and services that support people's daily activities and enable friendly, efficient and trustworthy interactions between humans and between humans and autonomous applications in smart environments. However, such interactions require understanding of the people's intentions, motions, and interactions, as well as knowledge about actions, objects, and their relations and roles in the human activities. While neural network based AI achieved recognition of objects and motions in many applications in some extent, integration of knowledge required to interpret human daily activity into data driven AI still remains to be an ambitious challenge.
To develop AI systems for real-world situations, it is necessary to recognize and interpret human daily activities, integrate them to structured knowledge (manuals, safety guidelines, law, etc.) not embodied in the observed data and generate intelligent reactions. X to Knowledge Graph technology integrates structured knowledge into multimodal and sensor observation data to provide a computational framework, which enables us to develop AI technologies that exploits hybrid modeling techniques (rule-based and neural models) for AI-based interaction management. Using such technologies, the DKI team is working on uncovering the semantics of human behaviour, and developing AI technology that can recognize and support various episodes of daily activities by integrating observation data and knowledge into a context-aware dialogue model that allows natural interaction in a variety of cases where it is necessary for humans to have dialogue interactions with other agents and with the smart environment.

Ken Fukuda,
Team Leader
Information
List of Publications
Fukuda, K., Julio, R., Nishimura, S. : Massive Semantic Video Annotation in High-end Customer Service - Example in Airline Service Value Assessment. In: Proceedings on HCI International 2020. (2020)
Oshiyama, C., Niwa, S., Jokinen, K., Nishimura, T. (2020). Development of a Dialogue System
that Supports Recovery for Patients with Schizophrenia. IWSDS-2020.
Researcher Profile
Photo | Name and role | Field of Expertise | E-mail address HP |
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Team Leader Ken Fukuda |
Knowledge representation, Ontology | |
Senior Researcher Jokinen Kristiina |
dialogue modelling, multimodal communication (gaze, gesturing), human-robot interaction | ||
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Researcher Satoshi Nishimura |
Ontology engineering, Knowledge engineering, Serviceology | |
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Researcher Shusaku Egami |
Semantic Web, Ontology, Knowledge Graph Embedding | |
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Post-Doctrial Researcher Julio Vizcarra |
Knowledge graphs, natural language processing, pattern recognition, data mining, GIS, spatial analysis. graph computation. | |
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Invited Senior Researcher |
Ontology Engineering, Semantic Technology | |
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Invited Senior Researcher |
Knowledge Acquisition and Representation,Knowledge Graph,Semantic Web,Ontology | |
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Research Assistant Taiga Mori |
dialogue modelling,multimodal interaction,cognitive science | |
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Research Assistant Yuto Tsukagoshi |
Semantic Web, Ontology | |
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Invited Researcher |
Semantic information and metadata modelling; Information and content management; Digital information services; | |
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Invited Researcher |
Semantic Web, Ontology Engineering, Knowledge Engineering |