2022 Fiscal Year Research-status Report
Temporal Knowledge Graph Construction from Text
Project/Area Number |
21K17816
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Research Institution | Japan Advanced Institute of Science and Technology |
Principal Investigator |
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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Project Period (FY) |
2021-04-01 – 2024-03-31
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Keywords | Temporal Knowledge Graph / Knowledge Extraction / Financial Report / Financial Application |
Outline of Annual Research Achievements |
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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Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
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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Strategy for Future Research Activity |
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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Causes of Carryover |
We could not attend the international conference due to the difficulty of traveling last year. Considering this situation, we plan to use the rest of the budget for travel expenses this year.
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Research Products
(2 results)