15 citations · 23 across the 10 of their papers we have counts for
10 papers
HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities
Shusaku Egami, Aoi Ohta, Tomoki Tsujimura +5
Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured knowledge. Integrating these two…
A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web
Shusaku Egami, Masahiro Hamasaki
The evolution of Large Language Models (LLMs) and the software agents built on them (AI agents) marks a turning point in the transition from a human-centric Web to an ``Agentic Web…
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…
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…
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Shusaku Egami, Takanori Ugai, Masateru Oota +4
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, obj…
CIRO: COVID-19 infection risk ontology
Shusaku Egami, Yasunori Yamamoto, Ikki Ohmukai +1
Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronaviru…
Synthesizing Event-centric Knowledge Graphs of Daily Activities Using Virtual Space
Shusaku Egami, Takanori Ugai, Mikiko Oono +2
Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-physical systems that can understand the various contextual information of daily life…
Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System
Takahiro Kawamura, Shusaku Egami, Koutarou Tamura +11
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 pro…