Knowledge Graph Completion Using Multiple Embedding Representations for Intelligent Information Extraction from Technical Reports
Pages 15 - 21
Abstract
As a new data structure, knowledge graphs are widely used in search engines, recommendation systems, question and answer systems and other related fields. The knowledge graph is helpful to realize intelligent and digital knowledge service of nuclear power accidents, in which link prediction can solve the discovery and restoration of missing information in Knowledge graph. This is also one of the research hotspot in the field of knowledge graph applications. Currently, the text description of entity information is rarely considered in the completion of nuclear power Knowledge graph. In this paper, we propose MEK-ConvKB (Multi-Embedding Knowledge Graph Prediction based on ConvKB), a reasoning model combined with multi-embedding techniques to improve performance. The embedded expression of Knowledge graph is enhanced by text description in nuclear power accident reports, which improves the accuracy of link prediction and expands the Knowledge graph of nuclear power accidents. The results show that our model can effectively express the semantic association between entities, Our model achieved the best performance compared to the baseline methods., which can provide a research basis for solving the discovery and restoration of missing information in knowledge graphs.
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- Knowledge Graph Completion Using Multiple Embedding Representations for Intelligent Information Extraction from Technical Reports
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Published In
August 2023
301 pages
ISBN:9798400708206
DOI:10.1145/3625156
Copyright © 2023 ACM.
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Published: 21 November 2023
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ICISS 2023
ICISS 2023: 2023 The 6th International Conference on Information Science and Systems
August 11 - 13, 2023
Edinburgh, United Kingdom
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