Semantic Annotation of Scientific Publications Based on Integration of Concept Knowledge

  • Shwe Sin PhyoEmail author
  • Nyein Nyein MyoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


Discovery of knowledge plays a crucial role in large volumes of data for extracting the valuable knowledge units. The indexing activity with the meaning of contents instead of character strings has become the motivation of searching documents in the information retrieval field. The process of finding and selecting the relevant concepts are the main objectives for the semantic indexing activity. This paper proposes a semantic annotation strategy to support the semantic indexing activity of academic community. The proposed activity extracts the corresponding concepts of a specific document from the semantic network. The annotation activity is based on the semantic degree value of each concept. The knowledge-based approach is used to calculate the degree value of concepts and this approach only rely on the concepts structure of knowledge graph. The proposed annotation activity can be applied as part of the semantic web application and semantic search engines for analyzing and characterizing the meanings of contents.


Semantic annotation WordNet ontology Semantic similarity Taxonomic knowledge 


  1. 1.
    Lu, M., Bangalore, S., Cormode, G., Hadjieleftheriou, M., Srivastava, D.: A dataset search engine for the research document corpus. In: IEEE 28th International Conference on Data Engineering. IEEE (2012)Google Scholar
  2. 2.
    Abramowicz, W. (ed.): Knowledge-Based Information Retrieval and Filtering from the Web. Springer, Heidelberg (2013)zbMATHGoogle Scholar
  3. 3.
    Nebot, V., Berlanga, R.: Exploiting semantic annotations for open information extraction: an experience in the biomedical domain. Knowl. Inf. Syst. 38(2), 365–389 (2014)CrossRefGoogle Scholar
  4. 4.
    Albukhitan, S., Alnazer, A., Helmy, T.: Semantic annotation of arabic web documents using deep learning. Procedia Comput. Sci. 130, 589–596 (2018)CrossRefGoogle Scholar
  5. 5.
    Rahman, F., Siddiqi, J.: Semantic annotation of digital music. J. Comput. Syst. Sci. 78(4), 1219–1231 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Aronson, A.R., Lang, F.M.: An overview of MetaMap: historical perspective and recent advances. J. Am. Med. Inform. Assoc. 17(3), 229–236 (2010)CrossRefGoogle Scholar
  7. 7.
    Dai, M., Shah, N.H., Xuan, W.: An efficient solution for mapping free text to ontology terms. AMIA Summit on Translational Bioinformatics, San Francisco, CA (2008)Google Scholar
  8. 8.
    Agirre, E., López de Lacalle, O., Soroa, A.: Random walks for knowledge-based word sense disambiguation. Computational Linguistics, 40(1), 57–84 (2014)Google Scholar
  9. 9.
    Navigli, R., Lapata, M.: An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 678–692 (2009)CrossRefGoogle Scholar
  10. 10.
    Pavlovskiy, I.S.: Using concepts of scientific activity for semantic integration of publications. Procedia Comput. Sci. 103, 370–377 (2017)CrossRefGoogle Scholar
  11. 11.
    Hood, Z., Sahari, N.: Researchers annotation collections and practices. Procedia Technol. 11, 354–358 (2013)CrossRefGoogle Scholar
  12. 12.
    Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)CrossRefGoogle Scholar
  13. 13.
    Elavarasi, S.A., Akilandeswari, J., Menaga, K.: A survey on semantic similarity measure. Int. J. Res. Advent Technol. 2(3), 389–398 (2014)Google Scholar
  14. 14.
    Poorna, B., Ramkumar, A.S.: Semantic similarity measures: an overview and comparison. Int. J. Adv. Res. Comput. Sci. 9(1), 100 (2018)CrossRefGoogle Scholar
  15. 15.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)Google Scholar
  16. 16.
    Castells, P., Fernandez, M., Vallet, D.: An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2006)CrossRefGoogle Scholar
  17. 17.
    Ruiz-Martínez, J.M., Valencia-García, R., Fernández-Breis, J.T., García-Sánchez, F., Martínez-Béjar, R.: Ontology learning from biomedical natural language documents using UMLS. Expert Syst. Appl. 38(10), 12365–12378 (2011)CrossRefGoogle Scholar
  18. 18.
    Teixeira, M.A.C., Belloze, K.T., Cavalcanti, M.C., Silva-Junior, F.P.: Data mart construction based on semantic annotation scientific articles, a case study for the prioritization of drug targets. Comput. Methods Programs Biomed. 157, 225–235 (2018)CrossRefGoogle Scholar
  19. 19.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI. Vol. 6. (2006)Google Scholar
  20. 20.
    Martin, J.H., Jurafsky, D.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, And Speech Recognition. Pearson/Prentice Hall, Upper Saddle River (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Computer StudiesMandalayMyanmar

Personalised recommendations