Research on relationship extraction for constructing knowledge graphs

Chang Lou; Xiaoxia Jia; Xiaokai Xia

doi:10.1117/12.2656649

20 October 2022 Research on relationship extraction for constructing knowledge graphs

Chang Lou, Xiaoxia Jia, Xiaokai Xia

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245120 (2022) https://doi.org/10.1117/12.2656649
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

A knowledge graph, consisting of concepts, entities, and their relationships, is the most effective way to handle vast amounts of information. This study presents a relationship extraction approach for designing and constructing a graph structure, given that current domain knowledge graphs are unable to identify links between various kinds of data structures utilizing particular building methods. Data from the Network Encyclopedia's categorization system and web page classification labels, both structured and semi-structured, provide the basis for hyponymy. Non-superior connections are covered as well. The convolution residual network that is based on cross-entropy loss function has also been enhanced as a consequence of the suggested solution. Using a comparable existing item as a reference, we evaluate the design to see whether it performs as predicted.

Citation Download Citation

Chang Lou, Xiaoxia Jia, and Xiaokai Xia "Research on relationship extraction for constructing knowledge graphs", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245120 (20 October 2022); https://doi.org/10.1117/12.2656649

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