研究課題/領域番号 |
21K17816
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研究種目 |
若手研究
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配分区分 | 基金 |
審査区分 |
小区分61030:知能情報学関連
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研究機関 | 北陸先端科学技術大学院大学 |
研究代表者 |
K. Natthawut 北陸先端科学技術大学院大学, 先端科学技術研究科, 助教 (40818100)
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研究期間 (年度) |
2021-04-01 – 2024-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
3,510千円 (直接経費: 2,700千円、間接経費: 810千円)
2022年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2021年度: 2,340千円 (直接経費: 1,800千円、間接経費: 540千円)
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キーワード | Temporal Knowledge Graph / Knowledge Extraction / Financial Report / Financial Application / Knowledge Graph / Knowledge Representation / Relation Extraction / Event Extraction |
研究開始時の研究の概要 |
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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研究実績の概要 |
This research aims to build a temporal knowledge graph from the text. In this fiscal year, we constructed FinKG, the temporal knowledge graph in the financial domain using information reported from SEC and market exchange. The temporal information including the report detail and stock price, is extracted using our financial ontology as a template. The temporal information is encoded at the edge of the knowledge graph. Overall, FinKG contained more than 30 million facts. Furthermore, we demonstrate the usefulness of FinKG with two applications: knowledge retrieval and stock price prediction. Knowledge retrieval reveals the complex connection among entities, while aggregated features from FinKG help neural models to better forecast stock prices.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
The research project progressed as scheduled. We built the temporal knowledge graph as planned. Also, we could demonstrate the application of temporal knowledge graph in financial domain.
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今後の研究の推進方策 |
Currently, we use the rule-based method to extract the temporal clue from text. In the future, We plan to further investigate and improve the temporal clue extraction from text.
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