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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Published in:IEEE transactions on big data pp. 1 - 13
Main Authors: Bai, Luyi, Dong, Jixuan, Zhu, Lin
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
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
ISSN:2332-7790, 2372-2096
Online Access:Get full text
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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.
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
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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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