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Knowledge Graph Completion Using Multiple Embedding Representations for Intelligent Information Extraction from Technical Reports

Published: 21 November 2023 Publication History

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.

References

[1]
Dong X, Gabrilovich E, Heitz G, Knowledge vault: A web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014: 601-610.
[2]
Balažević I, Allen C, Hospedales T M. Tucker: Tensor factorization for knowledge graph completion[J]. arXiv preprint arXiv:1901.09590, 2019.
[3]
Nguyen D Q. An overview of embedding models of entities and relationships for knowledge base completion[J]. arXiv preprint arXiv:1703.08098, 2017.
[4]
Trouillon T, Welbl J, Riedel S, Complex embeddings for simple link prediction[C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48. 2016: 2071-2080.
[5]
Bordes A, Usunier N, Garcia-Duran A, Translating embeddings for modeling multi-relational data[J]. Advances in neural information processing systems, 2013, 26.
[6]
Bordes A, Usunier N, Garcia-Duran A, Translating embeddings for modeling multi-relational data[J]. Advances in neural information processing systems, 2013, 26.
[7]
Wang Z, Zhang J, Feng J, Knowledge graph embedding by Translating on hyperplanes[C]//Proceedings of the AAAI conference on artificial intelligence. 2014, 28(1).
[8]
Lin Y, Liu Z, Sun M, Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the AAAI conference on artificial intelligence. 2015, 29(1).
[9]
Dettmers T, Minervini P, Stenetorp P, Convolutional 2d knowledge graph embeddings[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).
[10]
Dai Quoc Nguyen T D N, Nguyen D Q, Phung D. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network[C]//Proceedings of NAACL-HLT. 2018: 327-333.
[11]
Nathani D, Chauhan J, Sharma C, Learning attention-based embeddings for relation prediction in knowledge graphs[J]. arXiv preprint arXiv:1906.01195, 2019.
[12]
Vashishth S, Sanyal S, Nitin V, Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(03): 3009-3016.
[13]
Kenton J D M W C, Toutanova L K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of NAACL-HLT. 2019: 4171-4186.
[14]
Wang Z, Zhang J, Feng J, Knowledge graph and text jointly embedding[C]//Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014: 1591-1601.
[15]
Xie R, Liu Z, Jia J, Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2016, 30(1).
[16]
Xiao H, Huang M, Meng L, SSP: semantic space projection for knowledge graph embedding with text descriptions[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2017, 31(1).
[17]
Rossi A, Barbosa D, Firmani D, Knowledge graph embedding for link prediction: A comparative analysis[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021, 15(2): 1-49.

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ICISS '23: Proceedings of the 2023 6th International Conference on Information Science and Systems
August 2023
301 pages
ISBN:9798400708206
DOI:10.1145/3625156
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Association for Computing Machinery

New York, NY, United States

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Published: 21 November 2023

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