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Egami Lab
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An AIST-collaborative lab that structures the meaning of data using knowledge graphs.
AboutAbout the lab
Organizing data into knowledge to advance AI.
The Egami Lab studies technologies that extract meaning and context from large amounts of data and organize it as knowledge.
Centered on knowledge graphs, which represent relationships between data as graphs, and on Linked Data, their web standard, the lab also combines machine learning and natural language processing.
Its goal is to create a cyberspace in which all kinds of data are structured as knowledge, and to advance today's web and AI and apply them to solving real-world problems.
Collaborative Associate Professor Egami is affiliated with the National Institute of Advanced Industrial Science and Technology (AIST), and at the University of Electro-Communications the lab collaborates with the Osuga-Tahara-Sei Lab.
Key terms
- Knowledge graph
- A representation of relationships between data as a network of nodes and edges (a graph)
- Linked Data
- A standard for representing knowledge graphs on the web. It is one of the Semantic Web technologies
- Ontology
- An organized and structured representation of expert knowledge in a field, or of abstract knowledge shared across fields
ResearchResearch themes
As the variety of data keeps growing, the lab works on technologies that extract the meaning and context of data and structure and systematize it so that AI and information systems can use it. Centered on knowledge graphs and Semantic Web technologies, it carries out research that combines knowledge representation, machine learning, and natural language processing.
*Illustrative imageBuilding and Using Linked Data
The lab automatically builds knowledge graphs from government data, web articles, social media, sensor data, and more, and publishes them as Linked Open Data. This research is highly public in nature, as it uses these data to support solutions to social problems.
*Illustrative imageStructuring Knowledge and Reasoning
The lab builds ontologies that organize expert knowledge in specific fields as well as abstract knowledge shared across fields. These are applied to improving interoperability between different information systems and to making search more efficient through reasoning.
*Illustrative imageMachine Learning Applications of Knowledge Graphs
The lab works on research that applies knowledge graphs to machine learning tasks such as classification, clustering, prediction, and recommendation, using representation learning that embeds knowledge graphs in vector spaces.
*Illustrative imageAI That Can Explain Its Reasoning
The lab studies AI systems that can logically explain the reasons and grounds for their decisions. It also holds the Knowledge Graph Reasoning Challenge, for which it builds and provides knowledge graphs.
MembersFaculty
- Affiliated Associate ProfessorShusaku Egami
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Archaeological Linked Data Cloud (inverse)” Florian Thiery / CC BY 4.0 (cropped)
- Building and Using Linked Data: “Wikidata in the Linked Open Data cloud” Thomas Shafee / CC BY 4.0 (cropped)
- Structuring Knowledge and Reasoning: “SimpleSemanticDataMiningDiagram” HB181999 / CC BY-SA 4.0 (cropped)
- Machine Learning Applications of Knowledge Graphs: “KnowledgeGraphEmbedding” EdoardoRamalli / CC BY-SA 4.0 (cropped)
- AI That Can Explain Its Reasoning: “Schema Deduzione Induzione” Salnitrum / CC0 (cropped)
- Related lab (兼岩 憲 研究室): “Bookshelves in Faculty of Arts Library, Chulalongkorn University” Geonuch / CC BY-SA 4.0 (cropped)
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- Related lab (小泉 直也 研究室): “Stylised flower as light installation in public park Rudaki, Dushanbe, Tajikistan” Stesso / CC0 (cropped)
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