A knowledge graph visualization and retrieval system based on joint extraction model

Jingtang Luo; Shiying Yao; Ke Miao; Shuang Zhou; Yuren Feng; Jie Xu

doi:10.1117/12.2656590

20 October 2022 A knowledge graph visualization and retrieval system based on joint extraction model

Jingtang Luo, Shiying Yao, Ke Miao, Shuang Zhou, Yuren Feng, Jie Xu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245121 (2022) https://doi.org/10.1117/12.2656590
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Knowledge graph is a kind of storage structure which conforms to human cognition form and can accurately describe the relationship information between things. In this paper, a visualization and retrieval system of knowledge graph is built starting with the original Textual corpus. This paper first analyzes the requirements of the visualization and retrieval system. Then in the system implementation stage, in order to realize the visualization and retrieval system, we divide the system into three modules (Data Module, Visualization Module and Query Module) and implement them, respectively. For data module, we based on the entity relation joint extraction model processing text data into relational data (triples). For visualization module, we visualized the triples into node-link diagram based on the force-directed algorithm. Apart from this, we realized some interactive functions based on the D3.js visual framework and HTML technology.

Citation Download Citation

Jingtang Luo, Shiying Yao, Ke Miao, Shuang Zhou, Yuren Feng, and Jie Xu "A knowledge graph visualization and retrieval system based on joint extraction model", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245121 (20 October 2022); https://doi.org/10.1117/12.2656590

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