A Knowledge Graph Enhanced Semantic Matching Method for Plan Recommendation
| Autor: | Kebo Deng, Shaoqiu Zheng, Zhengwei Zhang, Rupeng Liang, Wei Ma, Zexiang Mao |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030788100 ICSI (2) |
| DOI: | 10.1007/978-3-030-78811-7_28 |
| Popis: | In order to solve the problem of rapid matching and optimization of the plan, a semantic feature-based smart matching method of the plan is proposed, which can be used to solve the problem of the typical small sample date for recommendation of the best plan in the military field. In this method, the semantic feature of the battle plan is established to describe the combat scenario through a military knowledge graph. The semantic feature annotation of the plan is constructed based on the military knowledge map too. So the semantic features corresponding to each matching target plan object are described, which realize the explicit definition of the hidden knowledge of the combat plan. Based on knowledge enhancement technology, the similarity measurement of semantic features is calculated, realizing the intelligent semantic matching of combat plans, so as to solve the problem of low matching efficiency and accuracy based on pragmatic level features such as index or keywords, satisfy the rapid matching and precise recommendation of plans. |
| Databáze: | OpenAIRE |
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