Abstract
As a semantic knowledge base, knowledge graph is a powerful tool for managing large-scale knowledge consists with instances, concepts and relationships between them. In view that the existing domain knowledge graphs can not obtain relationships in various structures through targeted approaches in the process of construction which resulting in insufficient knowledge utilization, this paper proposes a relationship extraction method for domain knowledge graph construction. We obtain upper and lower relationships from structured data in the classification system of network encyclopedia and semi-structured data in the classification labels of web pages, and non-superordinate relationships are extracted from unstructured text through the proposed convolution residual network based on improved cross-entropy loss function. We verify the effectiveness of the designed method by comparing with existing relationship extraction methods and constructing a food domain knowledge graph.
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References
Agichtein E, Gravano L, et al. Snowball: extracting relations from large plain-text collections[J]. 2000:85–94
Amarilli A, Galárraga L, Preda N, et al. Recent Topics of Research around the YAGO Knowledge Base [M]. Web Technologies and Applications. Springer International Publishing, 2014: 1–12
Cohen W, Cohen W. Personalized recommendations using knowledge graphs: a probabilistic logic programming approach[C]. ACM Conf. Recommend. Syst. ACM, 2016:325–332
Gupta M, Bendersky M. Information Retrieval with Verbose Queries[C]. International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015:1121–1124
Hasegawa T, Sekine S, Grishman R. Discovering relations among named entities from large corpora[C]. Meeting on Association for Computational Linguistics. Association for Computational Linguistics. 2004:415–422
Hoffmann R, Zhang C, Ling X, et al. Knowledge-based weak supervision for information extraction of overlapping relations[C]. The, Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19–24 June, 2011, Portland, Oregon, USA. DBLP, 2011:541–550
Huang Y Y, Wang W Y. Deep residual learning for weakly-supervised relation extraction[J]. arXiv preprint arXiv:1707.08866, 2017
IMDB Official. IMDB[EB/OL]. [2016-02-27]. http://www.imdb.com
Laina I, Rupprecht C, Belagiannis V, et al. Deeper depth prediction with fully convolutional residual networks[C]//2016 fourth international conference on 3D vision (3DV). IEEE, 2016: 239–248
Lee Y H, Koh J L. Conditional relationship extraction for diseases and symptoms by a web search-based approach[C]//2018 IEEE/WIC/ACM international conference on web intelligence (WI). IEEE, 2018: 554–561
Lehmann J. DBpedia: A Nucleus for a Web of Open Data[C]. The Semantic Web, International Semantic Web Conference, Asian Semantic Web Conference, ISWC 2007 + Aswc 2007, Busan, Korea, November. 2007:11--15
Liang C, Ye J, Wu Z, et al. Recovering Concept Prerequisite Relations from University Course Dependencies[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017
Lin Y, Liu Z, Zhu X, et al. Learning entity and relation embeddings for knowledge graph completion[C]. Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2015:2181–2187
Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]// ACL 2016. Berlin: [s. n.]. 2016: 2124–2133
Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proc. IEEE Int. Conf. Comput. Vision. 2017: 2980–2988
Lv Q, Xu L, Yu J, et al. Research on domain knowledge graph based on the large scale online knowledge fragment[C]. Adv. Res. Technol. Indust. Appl. IEEE, 2014:312–315
MetaBrainz Foundation. Musicbrainz[EB/OL]. [2016-06-06]. http://musicbrainz.org/
Mintz M, Bills S, Snow R, et al. Distant supervision for relation extraction without labeled data[C]. Joint Conference of the, Meeting of the ACL and the, International Joint Conference on Natural Language Processing of the Afnlp: Volume. Association for Computational Linguistics, 2009:1003–1011
Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]. Meeting of the Association for Computational Linguistics. 2016:1105–1116
Necesal P, Pospıšil J. Experience with teaching mathematics for engineers with the aid of Wolfram Alpha[C]//Proc. World Cong. Eng. Comput. Sci. 2012, 1: 271–274
Ningthoujam D, Yadav S, Bhattacharyya P, et al. Relation extraction between the clinical entities based on the shortest dependency path based LSTM[J]. arXiv preprint arXiv:1903.09941, 2019
Pan L, Li C, Li J, et al. Prerequisite relation learning for concepts in moocs[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017: 1447–1456
Pantel P, Pennacchiotti M. Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations.[C]. The, International Conference on Computational Linguistics and, Meeting of the ACL. 2006:113–120
Pennington J, Socher R, Manning C. Glove: Global vectors for word representation[C]//Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014: 1532–1543
Ravichandran D, Hovy E. Lerning surface text patterns for a question answering system[C]. Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2013:41–47
Riedel S, Yao L, McCallum A. Modeling Relations and their Mentions without Labeled Text[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010: 148–163
Sanderson M, Croft B. Deriving Concept Hierarchies from Text[R]. Massachusetts Univ Amherst Dept of Computer Science, 2005
Socher R, Huval B, Manning CD, et al. Semantic compositionality through recursive matrix-vector spaces[C]. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012:1201–1211
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting[J]. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Sudo K, Sekine S, Grishman R. An improved extraction pattern representation model for automatic IE pattern acquisition[C]. Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2003:224–231
Surdeanu M, Tibshirani J, Nallapati R, et al. Multi-instance multi-label learning for relation extraction[C]. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012:455–465
Wang Z, Huang J, Li H, et al. Probase: a Universal Knowledge Base for Semantic Search[J]. 2004
Wang S, Ororbia A, Wu Z, et al. Using prerequisites to extract concept maps fromtextbooks[C]//proceedings of the 25th acm international on conference on information and knowledge management. ACM, 2016: 317–326
Yih W T, Chang M W, He X, et al. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base[C]. Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing. 2015:1321–1331
Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[C]. Int. Conf. Comput. Linguist. 2014:2335–2344
Zeng D, Liu K, Chen Y, et al. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks[C]// EMNLP 2015. Lisbon: [s. n.]. 2015: 1753-1762
Zhang M, Su J, Wang D, et al. Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering[M]. Natural Language Processing – IJCNLP 2005. Springer Berlin Heidelberg, 2005:378–389
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (No. 61877002, No.61532006, No.61772083), Beijing Municipal Commision of Education PXM2019_014213_000007, Special subject of Innovation Method Work of the Ministry of Science and Technology (2018IM020200), The National Social Science Fund of China (18BGL202) and The Social Science and Humanity on Young Fund of the ministry of Education (17YJCZH127).
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This article belongs to the Topical Collection: Special Issue on Graph Data Management in Online Social Networks
Guest Editors: Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, and Junping Du
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Yu, H., Li, H., Mao, D. et al. A relationship extraction method for domain knowledge graph construction. World Wide Web 23, 735–753 (2020). https://doi.org/10.1007/s11280-019-00765-y
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DOI: https://doi.org/10.1007/s11280-019-00765-y