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xupike f81c12939c | 2 years ago | |
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Entity-centric Calculation Reasoning.json | 2 years ago | |
Entity-centric Logic Reasoning.json | 2 years ago | |
Entity-centric Quantity Reasoning.json | 2 years ago | |
Entity-centric Simple.json | 2 years ago | |
Event-centric Quantity Reasoning.json | 2 years ago | |
Event-centric Simple.json | 2 years ago | |
Event-centric-Probability Reasoning.json | 2 years ago |
Facts in military field tend to involve elements of time, space, quantity, status, and so on. Existing methods of representing knowledge in the form of triples fail to adequately express these facts, and also cause obstacle to knowledge storage and updating. Furthermore, question answering on these facts introduces new complexity dimension, which are complicated to be supported by existing corpus. Thus, we construct a Chinese knowledge base for military field covering entities and events centric knowledge, referred as MilKB. It consists of 965 entities and 3,017 facts. Moreover, we classify the natural questions into 26 types and construct a complex question answering dataset derived from MilKB, referred as MilKBQA. It consists of 2,829 questions, in which 600 are event-centric questions.
Python
MIT
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