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Construction and Completion of Document-Level Multimodal Question and Answer Knowledge Graph

In: Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024)

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  • Xiaoyi Zhang

    (University of Petroleum, School of Economics and Management)

Abstract

To make full use of the documents in question and answer (Q&A) community with text and image information included, improving the ability of information retrieval and semantic understanding, this paper focuses on the construction and completion of a multimodal Q&A knowledge graph. Firstly, we propose a document-level multimodal question and answer knowledge graph (DMQAKG), using topic, question, and answer documents as nodes, and building document relations. Furthermore, we also propose a multimodal Q&A knowledge graph completion method (MQAKGC) for DMQAKG based on multimodal feature extraction and fusion and multimodal knowledge graph link prediction. We use graph convolutional network (GCN) to capture the constructional features and long-short term memory (LSTM) to learn the chronological dependency between the entities for further completion of the missing relations. Experimental results show the superior performance of the proposed knowledge graph completion method in different Q&A subset scales.

Suggested Citation

  • Xiaoyi Zhang, 2024. "Construction and Completion of Document-Level Multimodal Question and Answer Knowledge Graph," Advances in Economics, Business and Management Research, in: Kun Zhang & Hang Luo & Tang Yao & Hongbo Li (ed.), Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024), pages 467-473, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-638-3_46
    DOI: 10.2991/978-94-6463-638-3_46
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