Temporal Knowledge Graph Question Answering Method Based on#br#
Semantic and Structural Enhancement

doi:10.3969/j.issn.1006-2475.2024.03.003

Abstract

Abstract: Abstract： Knowledge graphs， as one of the popular research topics in the field of natural language processing， have consistently received widespread attention from the academic community. In reality， the knowledge quiz process often carries temporal information. Consequently， in recent years， the application of temporal knowledge graphs for knowledge question answering has gained popularity among scholars. Traditional methods for temporal knowledge graph question answering primarily encode the question information to facilitate the inference process. However， they are unable to deal with the more complex entities and temporal relationships contained in the questions. To address this， semantic and structural enhancement for temporal knowledge graph question answering is proposed. This method aims to simultaneously consider both semantic and structural information in the inference process to improve the probability of providing correct answers. Firstly， implicit temporal expressions in the questions are parsed， and the questions are rewritten using direct representations based on the information in the temporal knowledge graph. Additionally， the temporal information in the temporal knowledge graph is aggregated according to different time granularities based on the question set. Secondly， the semantic information of the questions is represented and fused based on entity and time information to enhance the learning of entity and time semantics. Subsequently， subgraphs are extracted based on the extracted entities， and the structural information of the subgraphs is captured using graph convolutional networks. Finally， the fused semantic and structural information of the questions are concatenated， and candidate answers are scored， with the entity receiving the highest score selected as the answer. Comparative tests on MultiTQ data sets show that the proposed model outperforms other baseline models.

Key words: Key words： semantic enhancement, structural enhancement, graph neural networks, temporal knowledge graph question answering

CLC Number:

TP391

HUANG Zheng-lin, DONG Bao-liang. Temporal Knowledge Graph Question Answering Method Based on#br# Semantic and Structural Enhancement[J]. Computer and Modernization, 2024, 0(03): 15-23.

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Temporal Knowledge Graph Question Answering Method Based on#br# Semantic and Structural Enhancement

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