6 papers
Embedding Method for Knowledge Graph with Densely Defined Ontology
Takanori Ugai
Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE model…
Comparison of Metadata Representation Models for Knowledge Graph Embeddings
Shusaku Egami, Kyoumoto Matsushita, Takanori Ugai +1
Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information in domains…
Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments
Takanori Ugai, Kensho Hara, Shusaku Egami +1
We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. Our question answering (QA) dataset measures…
VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities
Shusaku Egami, Takahiro Ugai, Swe Nwe Nwe Htun +1
Multi-modal knowledge graphs (MMKGs), which ground various non-symbolic data (e.g., images and videos) into symbols, have attracted attention as resources enabling knowledge proces…
A Logical Approach to Criminal Case Investigation
Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino
XAI (eXplanable AI) techniques that have the property of explaining the reasons for their conclusions, i.e. explainability or interpretability, are attracting attention. XAI is exp…
Synthetic Multimodal Dataset for Empowering Safety and Well-being in Home Environments
Takanori Ugai, Shusaku Egami, Swe Nwe Nwe Htun +3
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 co…