Abstract
An open information extraction approach for knowledge graph construction is presented. The motivation for the work is that large quantities of scholarly documents are available within many domains of discourse, and the subsequent challenge is to identify the most relevant articles concerning a particular topic. The proposed approach takes a document corpus and identifies triples within this corpus which are then processed to generate a literature knowledge graph. The extraction of triples is conducted using an open information extraction approach. The proposed OIE4KGC approach was evaluated using a bespoke clinical research methodology dataset and a benchmark dataset. A f-score of 51% was achieved on a clinical research methodology dataset and a f-score of 37% was achieved on the benchmark dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
The ORRCA (Online Resource for Recruitment Research in Clinical Trials) dataset is part of a PhD with the University of Liverpool’s Biostatistic’s department. This dataset will be released publicly on the author’s website.
- 4.
- 5.
- 6.
- 7.
References
Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction (2018)
Jinha, A.E.: Article 50 million: an estimate of the number of scholarly articles in existence. Learn. Publ. 23, 258–263 (2010)
Bodenreider, O.: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucl. Acids Res. 32, D267–D270 (2004)
Kearney, A., et al.: Development of an online resource for recruitment research in clinical trials to organise and map current literature. Clin. Trials 15, 533–542 (2018)
Yates, A., Cafarella, M., Banko, M., Etzioni, O., Broadhead, M., Soderland, S.: TextRunner. In: Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations on XX - NAACL 2007 (2007)
Weld, D.S., Hoffmann, R., Wu, F.: Using Wikipedia to bootstrap open information extraction. ACM SIGMOD Rec. 37, 62 (2009)
Fader, A., Zettlemoyer, L.: Paraphrase-driven learning for open question answering. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1608–1618 (2013)
Stanovsky, G., Michael, J., Zettlemoyer, L., Dagan, I.: Supervised open information extraction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (2018)
Cui, L., Wei, F., Zhou, M.: Neural open information extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2018)
Zhan, J., Zhao, H.: Span model for open information extraction on accurate corpus. https://arxiv.org/abs/1901.10879
Jiang, M., et al.: MetaPAD. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2017 (2017)
Qin, L., Hao, Z., Yang, L.: Question answering system based on food spot-check knowledge graph. In: Proceedings of 2020 the 6th International Conference on Computing and Data Engineering (2020)
Bhutani, N., Jagadish, H.V., Radev, D.: Nested propositions in open information extraction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)
Jaradeh, M.Y., et al.: Open research knowledge graph. In: Proceedings of the 10th International Conference on Knowledge Capture - K-CAP 2019 (2019)
Ammar, W., et al.: Construction of the literature graph in semantic scholar. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers) (2018)
White, A.S., et al.: Universal decompositional semantics on universal dependencies. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)
Corro, L.D., Gemulla, R.: ClausIE. In: Proceedings of the 22nd International Conference on World Wide Web - WWW 2013 (2013)
Zhao, S., Su, C., Sboner, A., Wang, F.: Graphene. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM 2019 (2019)
Huang, Z., Yang, J., van Harmelen, F., Hu, Q.: Constructing knowledge graphs of depression. In: Siuly, S., Huang, Z., Aickelin, U., Zhou, R., Wang, H., Zhang, Y., Klimenko, S. (eds.) HIS 2017. LNCS, vol. 10594, pp. 149–161. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69182-4_16
Han, L., Finin, T., Parr, C., Sachs, J., Joshi, A.: RDF123: from spreadsheets to RDF. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88564-1_29
Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., Morissette, J.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41, 706–716 (2008)
Haussmann, S., et al.: FoodKG: a semantics-driven knowledge graph for food recommendation. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 146–162. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_10
Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)
Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019)
Silva, V., Freitas, A., Handschuh, S.: Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition. https://www.aclweb.org/anthology/L18-1542/
Schmitz, M.: Open Language Learning for Information Extraction (2010). https://www.aclweb.org/anthology/D12-1048.pdf
Weld, D., Hoffmann, R., Wu, F.: Using Wikipedia to bootstrap open information extraction. ACM SIGMOD Rec. 37, 62 (2009)
Microsoft Academic Knowledge Graph. http://ma-graph.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Muhammad, I., Kearney, A., Gamble, C., Coenen, F., Williamson, P. (2020). Open Information Extraction for Knowledge Graph Construction. In: Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2020. Communications in Computer and Information Science, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-59028-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-59028-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59027-7
Online ISBN: 978-3-030-59028-4
eBook Packages: Computer ScienceComputer Science (R0)