| Project/Area Number |
21K17816
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Japan Advanced Institute of Science and Technology |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | Temporal Knowledge Graph / Financial KGs / Knowledge Representation / Relation Extraction / Knowledge Graph / Knowledge Acquisition / 金融 / Knowledge Extraction / Financial Report / Financial Application / Event Extraction |
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| Outline of Research at the Start |
Knowledge Graph plays a key role in various artificial intelligence applications. Generally, a knowledge graph is built to express static knowledge. Nevertheless, knowledge usually changes over time. A knowledge graph without considering the time does not satisfy the change. Therefore, this project aims to study a temporal knowledge graph and to develop a novel framework for constructing temporal knowledge graphs from text. The outcomes of this research will expand the study on the knowledge graph area and facilitate the time-aware knowledge graph-based applications.
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| Outline of Final Research Achievements |
The project outcomes include both the research methods and the development of temporal knowledge graphs in the financial domain. Specifically, we proposed a knowledge extraction framework that uses both semantic and syntactic features to extract useful knowledge from text, ensuring high-quality knowledge graphs. We constructed FinKG and FinKG-JP, temporal knowledge graphs in the financial domain. Temporal information, including report details and stock prices, was extracted using our financial ontology template and encoded at the edges of the knowledge graphs. We demonstrated the usefulness of FinKG in two applications: knowledge retrieval and stock price prediction.
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| Academic Significance and Societal Importance of the Research Achievements |
このプロジェクトでは、金融分野における時系列知識グラフであるFinKGとFinKG-JPの構築および開発方法を提案した。これらの時系列知識グラフは、金融分野における時間認識型AIアプリケーションの開発に貢献することができる。本研究の科学的意義は、時系列知識グラフの構築方法を進展させることであり、これは金融における予測分析や意思決定プロセスの向上に不可欠である。
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