Skip to main content
SpringerLink
Log in

Multiple order semantic relation extraction

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In order to get more comprehensive and accurate knowledge from the Semantic Web, it is essential to design an effective method to extract semantic data from the web and documents. However, as a crucial topic of semantic information extraction, relation extraction is a major challenge in knowledge base construction. Existing methods perform poorly on texts in the open domain due to their complex structure; this paper proposes a method named multiple order semantic relation extraction (MOSRE), which applies for multiple orders, a conceptual expression used in formal logistics, to build semantic patterns for extracting information from hybrid unstructured texts in the open domain with deep semantic analyses. Specifically, the proposed method automatically constructs a multiple order semantic tree from complex natural sentences and converts semantic information into a binary structure. Instead of constructing a large amount of pattern sets for comparing binary semantics between entities, MOSRE splits and reconstructs sentences into a strict hierarchical binary structure with combination rules in order to extract as much semantic information as possible. The extracted triples are then processed into several entity relations in the format 〈subject, relational label, object〉 after named entity recognition and refinement. MOSRE is validated by test results on two different datasets, achieving F1 values of 83.8% on SENT500 and 35.5% on KBP.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Akbik A, Visengeriyeva L, Herger P et al (2012) Unsupervised discovery of relations and discriminative extraction patterns. In: COLING, pp 17–32

  2. Angeli G, Premkumar MJ, Manning CD (2015) Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd annual meeting of the association for computational linguistics (ACL 2015)

  3. Angeli G, Tibshirani J, Wu J et al (2014) Combining distant and partial supervision for relation extraction. In: EMNLP, pp 1556–1567

  4. Banko M, Cafarella MJ, Soderland S et al (2007) Open information extraction from the web. In: IJCAI, vol 7, pp 2670–2676

  5. Banko M, Etzioni O (2008) The tradeoffs between open and traditional relation extraction. In: Proceedings of ACL-08: HLT, pp 28–36

  6. De Lacalle OL, Lapata M (2013) Unsupervised relation extraction with general domain knowledge. In: EMNLP, pp 415–425

  7. Del Corro L, Gemulla R (2013) Clausie: clause-based open information extraction. In: Proceedings of the 22nd international conference on World Wide Web. ACM, pp 355–366

  8. Dorow B, Widdows D, Ling K et al (2004) Using curvature and Markov clustering in graphs for lexical acquisition and word sense discrimination. arXiv preprint cond-mat/0403693

  9. Dutta A, Meilicke C, Stuckenschmidt H (2015) Enriching structured knowledge with open information. In: Proceedings of the 24th international conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp 267–277

  10. Fader A, Soderland S, Etzioni O (2011) Identifying relations for open information extraction. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1535–1545

  11. Han L, Kashyap AL, Finin T et al (2013) UMBC_EBIQUITY-CORE: semantic textual similarity systems. In: SEM@ NAACL-HLT, pp 44–52

  12. Kong B, Xu RF, Wu DY (2015) Bootstrapping-based relation extraction in financial domain. In: 2015 International conference on machine learning and cybernetics (ICMLC). IEEE, vol 2, pp 897–903

  13. Liu J, Rui W, Zhang L et al (2016) Social relation extraction with improved distant supervised and word embedding features. In: 2016 IEEE international conference on big data analysis (ICBDA). IEEE, pp 1–5

  14. Melamud O, Berant J, Dagan I et al (2013) A two level model for context sensitive inference rules. In: ACL (1), pp 1331–1340

  15. Min B, Shi S, Grishman R et al (2012) Ensemble semantics for large-scale unsupervised relation extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 1027–1037

  16. Moro A, Li H, Krause S et al (2013) Semantic rule filtering for web-scale relation extraction. In: International semantic web conference. Springer, Berlin, pp 347–362

    Chapter  Google Scholar 

  17. Nakashole N, Weikum G, Suchanek F (2012) PATTY: a taxonomy of relational patterns with semantic types. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 1135–1145

  18. Riedel S, Yao L, McCallum A et al (2013) Relation extraction with matrix factorization and universal schemas. In: HLT-NAACL, pp 74–84

  19. Rusu D, Hodson J, Kimball A (2014) Unsupervised techniques for extracting and clustering complex events in news. In: ACL 2014, p 26

  20. Surdeanu M, Tibshirani J, Nallapati R et al (2012) Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 455–465

  21. Wang J, Jing Y, Teng Y et al (2012) A novel clustering algorithm for unsupervised relation extraction. In: 2012 Seventh international conference on digital information management (ICDIM). IEEE, pp 16–21

  22. Xiang Y, Zhang Y, Wang X et al (2015) Bias modeling for distantly supervised relation extraction. Math Problems Eng. https://doi.org/10.1155/2015/969053

    Article  MathSciNet  MATH  Google Scholar 

  23. Yahya M, Whang S, Gupta R et al (2014) ReNoun: fact extraction for nominal attributes. In: EMNLP, pp 325–335

  24. Yao L, Riedel S, McCallum A (2012) Unsupervised relation discovery with sense disambiguation. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers—volume 1. Association for Computational Linguistics, pp 712–720

  25. Ye F, Shi H, Wu S (2014) Research on pattern representation method in semi-supervised semantic relation extraction based on bootstrapping. In: 2014 Seventh international symposium on computational intelligence and design (ISCID). IEEE, vol 1, pp 568–572

  26. Yu D, Huang H, Cassidy T et al (2014) The wisdom of minority: unsupervised slot filling validation based on multi-dimensional truth-finding. In: COLING, pp 1567–1578

  27. Zhang C, Xu W, Gao S et al (2014) A bottom-up kernel of pattern learning for relation extraction. In: 2014 9th International symposium on Chinese spoken language processing (ISCSLP). IEEE, pp 609–613

  28. Schmitz M, Bart R, Soderland S et al (2012) Open language learning for information extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 523–534

  29. Suchanek FM, Kasneci G, Weikum G (2007) Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 697–706

  30. Carlson A, Betteridge J, Kisiel B et al (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5, p 3

  31. Auer S, Bizer C, Kobilarov G et al (2007) Dbpedia: a nucleus for a web of open data. In: The semantic web, pp 722–735

    Chapter  Google Scholar 

  32. Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building Watson: an overview of the DeepQA project. AI magazine 31(3):59–79

    Article  Google Scholar 

  33. Zhou Q (2018) Multi-layer affective computing model based on emotional psychology. Electron Commer Res 18(1):109–124. https://doi.org/10.1007/s10660-017-9265-8

    Article  Google Scholar 

  34. Zhou Q, Luo J (2015) Artificial neural network based grid computing of E-government scheduling for emergency management. Comput Syst Sci Eng 30(5):327–335

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software Engineering Institute, Xidian University, Xi’an, 710071, China

    Shengli Song & Yulong Sun

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Shengli Song & Qiang Di

Authors
  1. Shengli Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yulong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Di
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengli Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, S., Sun, Y. & Di, Q. Multiple order semantic relation extraction. Neural Comput & Applic 31, 4563–4576 (2019). https://doi.org/10.1007/s00521-018-3453-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3453-x

Keywords

Search

Navigation