Advertisement

An Efficient Semantic Document Similarity Calculation Method Based on Double-Relations in Gene Ontology

  • Jingyu Hu
  • Meijing LiEmail author
  • Zijun Zhang
  • Kaitong Li
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)

Abstract

Semantic text mining is a challenging research topic in recent years. Many types of research focus on measuring the similarity of two documents with ontologies such as Medical Subject Headings (Mesh) and Gene Ontology (GO). However, most of the researches considered the single relationship in an ontology. To represent the document comprehensively, a semantic document similarity calculation method is proposed, based on utilizing Average Maximum Match algorithm with double-relations in GO. In the experiment, the results show that the double-relations based similarity calculation method is better than traditional semantic similarity measurements.

Keywords

Double-relations Semantic text similarity measure Document clustering Gene ontology 

Notes

Acknowledgements

This study was supported by the National Natural Science Foundation of China (61702324).

References

  1. 1.
    Danushka, B., Georgios, K., Sophia, A.: A cross-lingual similarity measure for detecting biomedical term translations. PLoS One 10(6), 7–15 (2015)Google Scholar
  2. 2.
    Spasić, I., Ananiadou, S.: A flexible measure of contextual similarity for biomedical terms. In: Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, pp. 197–208 (2005)Google Scholar
  3. 3.
    Rey-Long, L.: Passage-based bibliographic coupling: an inter-article similarity measure for biomedical articles. PLoS One 10(10), 6–10 (2015)Google Scholar
  4. 4.
    Chen, C., Hsieh, S., Weng, Y.: Semantic similarity measure in biomedical domain leverage Web Search Engine. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (2010)Google Scholar
  5. 5.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics (ACL’94), pp. 133–138 (1994)Google Scholar
  6. 6.
    Leacock, C., Chodorow, M.: Filling in a sparse training space for word sense identification. In: Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics (ACL94), pp. 248–256 (1994)Google Scholar
  7. 7.
    Li, Y., Bandar, Z., McLean, D.: An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. Knowl. Data Eng. Bioinform. 15(4), 871–882 (2003)CrossRefGoogle Scholar
  8. 8.
    Choudhury, J., Kimtani, D.K., Chakrabarty, A.: Text clustering using a word net-based knowledge-base and the Lesk algorithm. Int. J. Comput. Appl. 48(21), 20–24 (2012)Google Scholar
  9. 9.
    Lord, P., Stevens, R., Brass, A., Goble, C.: Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)CrossRefGoogle Scholar
  10. 10.
    Resnik, O.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity and natural language. J. Artif. Intell. Res. Bibliometr. 19(11), 95–130 (1999)CrossRefGoogle Scholar
  11. 11.
    Lin, D.: Principle-based parsing without overgeneration. In: 31st Annual Meeting of the Association for Computational Linguistics, pp. 112–120. Association for Computational Linguistics, USA (1993)Google Scholar
  12. 12.
    Zhang, X., Jing, L., Hu, X., et al.: A comparative study of ontology based term similarity measures on PubMed document clustering. In: International Conference on Database Systems, pp. 115–126. Springer, Berlin, Heidelberg (2007)Google Scholar
  13. 13.
    Jing, Z., Yuxuan, S., Shengwen, P., Xuhui, L., Hiroshi, M., Shanfeng, Z.: MeSHSim: an R/Bioconductor package for measuring semantic similarity over MeSH headings and MEDLINE documents. J. Bioinform. Comput. (2015) (BioMed Central)Google Scholar
  14. 14.
    Logeswari, S., Kandhasamy, P.: Designing a semantic similarity measure for biomedical document clustering. J. Med. Imaging Health Inform. 5(6), 1163–1170 (2015)CrossRefGoogle Scholar
  15. 15.
    The Gene Ontology Resource Home. http://geneontology.org/. Accessed 27 Feb 2019
  16. 16.
    Wang, J.Z., Du, Z., Payattakool, R., Yu, P.S., Chen, C.F.: A new method to measure the semantic similarity of go terms. Bioinformatics 23(10), 1274–1281 (2007)CrossRefGoogle Scholar
  17. 17.
    Zare, H., Shooshtari, P., Gupta, A., Brinkman, R.: Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinform. (2010)Google Scholar
  18. 18.
    Dongen, V.: A cluster algorithm for graphs. In: Information Systems, pp. 1–40. CWI (2000)Google Scholar
  19. 19.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD’96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
  20. 20.
    MacKay, D.: An example inference task: clustering. In: Information Theory, Inference and Learning Algorithms, pp. 284–292. Cambridge University Press (2003)Google Scholar
  21. 21.
    Robertson, S.: Understanding inverse document frequency: on theoretical arguments for IDF. J. Doc. 60(5), 503–520 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jingyu Hu
    • 1
  • Meijing Li
    • 1
    Email author
  • Zijun Zhang
    • 1
  • Kaitong Li
    • 1
  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiChina

Personalised recommendations