Intent-Driven Semantic Query: An Effective Approach for Temporal Knowledge Graph Query
The temporal knowledge graph (TKG) query facilitates the retrieval of potential answers by parsing questions that incorporate temporal constraints, regarded as a vital downstream task in the broader spectrum of the TKG applications. Currently, enhancing the accuracy of the queries and the user exper...
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| Vydáno v: | IEEE transactions on big data s. 1 - 13 |
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| Jazyk: | angličtina |
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2025
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| ISSN: | 2332-7790, 2372-2096 |
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| Abstract | The temporal knowledge graph (TKG) query facilitates the retrieval of potential answers by parsing questions that incorporate temporal constraints, regarded as a vital downstream task in the broader spectrum of the TKG applications. Currently, enhancing the accuracy of the queries and the user experience has become a focal point for researchers. Existing query methods of the TKG aim to execute unambiguous standard query statements to return query results while neglecting the potential ambiguity in user input queries. To overcome this problem, in this paper, we propose a semantic query model for temporal knowledge graphs, TKGSQ-PM (Temporal Knowledge Graph Semantic Query based on Pre-trained Model). This model first identifies and extracts entity and temporal information from temporal knowledge graph queries and obtains corresponding temporal knowledge graph embedding information based on embedding methods. Then, it utilizes the pre-trained model DistilBERT to infer the true query intent from user input queries. Finally, it performs comprehensive sorting to return highquality query results. We conduct multiple experiments on three different datasets to demonstrate the efficiency and effectiveness of the proposed methods. Experimental results indicate that the TKGSQ-PM model has an overall advantage over baseline models in terms of query effectiveness and efficiency. |
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| AbstractList | The temporal knowledge graph (TKG) query facilitates the retrieval of potential answers by parsing questions that incorporate temporal constraints, regarded as a vital downstream task in the broader spectrum of the TKG applications. Currently, enhancing the accuracy of the queries and the user experience has become a focal point for researchers. Existing query methods of the TKG aim to execute unambiguous standard query statements to return query results while neglecting the potential ambiguity in user input queries. To overcome this problem, in this paper, we propose a semantic query model for temporal knowledge graphs, TKGSQ-PM (Temporal Knowledge Graph Semantic Query based on Pre-trained Model). This model first identifies and extracts entity and temporal information from temporal knowledge graph queries and obtains corresponding temporal knowledge graph embedding information based on embedding methods. Then, it utilizes the pre-trained model DistilBERT to infer the true query intent from user input queries. Finally, it performs comprehensive sorting to return highquality query results. We conduct multiple experiments on three different datasets to demonstrate the efficiency and effectiveness of the proposed methods. Experimental results indicate that the TKGSQ-PM model has an overall advantage over baseline models in terms of query effectiveness and efficiency. |
| Author | Zhu, Lin Bai, Luyi Dong, Jixuan |
| Author_xml | – sequence: 1 givenname: Luyi surname: Bai fullname: Bai, Luyi email: baily@neuq.edu.cn organization: School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao, China – sequence: 2 givenname: Jixuan surname: Dong fullname: Dong, Jixuan email: dongjixuanneuq@hotmail.com organization: School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao, China – sequence: 3 givenname: Lin surname: Zhu fullname: Zhu, Lin email: zhulin@neuq.edu.cn organization: School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao, China |
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| Snippet | The temporal knowledge graph (TKG) query facilitates the retrieval of potential answers by parsing questions that incorporate temporal constraints, regarded as... |
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| SubjectTerms | Accuracy Cognition Data mining Intention Inference Knowledge graphs Pattern matching Pre-trained Model Semantic Query Semantics Structured Query Language Symbols Temporal Knowledge Graph Query Training Vectors |
| Title | Intent-Driven Semantic Query: An Effective Approach for Temporal Knowledge Graph Query |
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