Skip to main content
Springer Nature Link
Log in

Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning

  • Conference paper
  • First Online:
Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

Abstract

With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper introduces a novel relevance measurement for concepts, and it suggests new types of semantic relations. Also, it points out of using deep learning (DL) models for semantic relation extraction.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mishra, S., Jain, S.: A study of various approaches and tools on ontology. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology (CICT), pp. 57–61 (2015)

    Google Scholar 

  2. Maimon, O., Browarnik, A.: Ontology learning from text: why the ontology learning layer cake is not viable. Int. J. Signs Semiot. Syst. 4(2), 1–4 (2015)

    Google Scholar 

  3. Zou, X.: A survey on application of knowledge graph. J. Phys. Conf. Ser. 1487, 012016 (2020)

    Article  Google Scholar 

  4. Ergeta, M.: Introduction to Knowledge Graphs and their Applications (2019). https://medium.com/analytics-vidhya/introduction-to-knowledge-graphs-and-their-applications-fb5b12da2a8b. Accessed 30 Sept 2020

  5. da Silva, J.W.F., Venceslau, A.D.P., Sales, J.E., Maia, J.G.R., Pinheiro, V.C.M., Vidal, V.M.P.: A short survey on end-to-end simple question answering systems. Artif. Intell. Rev. 53(7), 5429–5453 (2020)

    Article  Google Scholar 

  6. Al-Ghuribi, S.M., Noah, S.A.M.: Multi-criteria review-based recommender system-the state of the art. IEEE Access. 7, 169446–169468 (2019)

    Article  Google Scholar 

  7. Franco, W., Avila, C.V.S., Oliveira, A., Maia, G., Brayner, A., Vidal, V.M.P., et al.: Ontology-based question answering systems over knowledge bases: a survey. In: ICEIS, vol. 1, pp. 532–539(2020)

    Google Scholar 

  8. Arnold, P., Rahm, E.: Extracting semantic concept relations from Wikipedia. In: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics, WIMS14, Thessaloniki, Greece, pp. 1–11. ACM (2014)

    Google Scholar 

  9. Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 697–706. ACM (2007)

    Google Scholar 

  10. Maedche, A., Volz, R.: The text-to-onto ontology extraction and maintenance environment. In: Proceedings of the ICDM-Workshop on Integrating Data Mining and Knowledge Management, San Jose, California, USA (2001)

    Google Scholar 

  11. Hahn, U., Romacker, M.: The SYNDIKATE text knowledge base generator. In: Proceedings of the 1st International Conference on Human Language Technology Research, San Diego, pp. 1–6. Association for Computational Linguistics (2001)

    Google Scholar 

  12. Hahn, U., Marko, K.G.: Ontology and lexicon evolution by text understanding. In: Proceedings of the ECAI 2002 Workshop on Machine Learning and Natural Language Processing for Ontology Engineering, OLT 2002, Lyon, France (2002)

    Google Scholar 

  13. Jiang, X., Tan, A.H.: CRCTOL: a semantic-based domain ontology learning system. J. Am. Soc. Inform. Sci. Technol. 61(1), 150–168 (2010)

    Article  Google Scholar 

  14. Gillani Andleeb, S.: From text mining to knowledge mining: an integrated framework of concept extraction and categorization for domain ontology, p. 146. Department of Information Systems, Budapesti Corvinus Egyetem, Budapest (2015)

    Google Scholar 

  15. Al-Ghuribi, S.M., Alshomrani, S.: Bi-languages mining algorithm for extraction useful web contents (BiLEx). Arab. J. Sci. Eng. 40(2), 501–518 (2015)

    Article  Google Scholar 

  16. Wong, W., Liu, W., Bennamoun, M.: Ontology learning from text: A look back and into the future. ACM Comput. Surv. (CSUR) 44(4), 20 (2012)

    Article  Google Scholar 

  17. Paiva, L., Costa, R., Figueiras, P., Lima, C.: Discovering semantic relations from unstructured data for ontology enrichment: association rules based approach. In: 2014 9th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2014)

    Google Scholar 

  18. Lee, L.-H., Lee, K.-C., Tseng, Y.-H.: The NTNU system at SemEval-2017 task 10: extracting keyphrases and relations from scientific publications using multiple conditional random fields. In: Proceedings of the 11th International Workshop on Semantic Evaluation, Vancouver, Canada, pp. 951–955. Association for Computational Linguistics (2017)

    Google Scholar 

  19. Al-Aswadi, F.N., Chan, H.Y., Gan, K.H.: Automatic ontology construction from text: a review from shallow to deep learning trend. Artif. Intell. Rev. 53(6), 3901–3928 (2020)

    Article  Google Scholar 

  20. Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Trans. Sig. Inf. Process. (2012)

    Google Scholar 

  21. Mo, D.: A survey on deep learning: one small step toward AI. Department of Computer Science, University of New Mexico, USA (2012)

    Google Scholar 

  22. Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning - a new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)

    Article  Google Scholar 

  23. Chen, X.-W., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access. 2, 514–525 (2014)

    Article  Google Scholar 

  24. Deng, L., Yu, D.: Deep learning. Sig. Process. 7, 3–4 (2014)

    Google Scholar 

  25. Hinton, G.E.: Deep belief networks. Scholarpedia 4(5), 5947 (2009)

    Article  Google Scholar 

  26. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7):1527–1554 (2006)

    Google Scholar 

  27. Fischer, A., Igel, C.: An introduction to restricted Boltzmann machines. In: Iberoamerican Congress on Pattern Recognition, pp. 14–36. Springer, Heidelberg (2012)

    Google Scholar 

  28. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5) (2018). Article 92

    Google Scholar 

  29. Dahab, M.Y., Hassan, H.A., Rafea, A.: TextOntoEx: automatic ontology construction from natural English text. Exp. Syst. Appl. 34(2), 1474–1480 (2008)

    Article  Google Scholar 

  30. Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) Natural Language Processing and Information Systems: Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, 15–17 June 2005, pp. 227–238. Springer, Heidelberg (2005)

    Google Scholar 

  31. Kolbe, N., Vandenbussche, P.-Y., Kubler, S., Traon, Y.L.: LOVBench: ontology ranking benchmark. In: Proceedings of the Web Conference 2020, Taipei, Taiwan, pp. 1750–1760. Association for Computing Machinery (2020).

    Google Scholar 

  32. Missikoff, M., Velardi, P., Fabriani, P.: Text mining techniques to automatically enrich a domain ontology. Appl. Intell. 18(3), 323–340 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia

    Fatima N. AL-Aswadi, Huah Yong Chan & Keng Hoon Gan

  2. Faculty of Computer Sciences and Engineering, Hodeidah University, Hodeidah, Yemen

    Fatima N. AL-Aswadi

Authors
  1. Fatima N. AL-Aswadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huah Yong Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keng Hoon Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huah Yong Chan .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

AL-Aswadi, F.N., Chan, H.Y., Gan, K.H. (2021). Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_35

Download citation

Publish with us

Policies and ethics