Email this article 
Open Access
Subscription or Fee Access
A Study on Relationship Extraction from Text Data
Abstract
The tremendous growth of Biomedical text mining increases the publications in literature. The task of Information Extraction is to identify the predefined set of concepts in a specific field. It will disregard unwanted irrelevant information’s. And recognizes the specific class of predefined entities, relationships and events. The manual identification of entity and relationships biomedical literature consumes much time and lengthy and laborious task. Automation of entity and relationship extraction addresses this issues. Various approaches are proposed to extract relationship from biomedical literature. This study analyses a range of approaches to automatic extraction of relationships from biomedical literature. It investigates various methods of relation Extraction System based on the working approach of the systems. The study includes the relation extraction approaches like Co-occurrence based approach, pattern based approach, Rule Based approach and machine Learning Based approaches. The outcomes of the systems are compared using the gold standards of text mining such as precision, recall and F-Measure.
Keywords
References
A. Ben Abacha and P. Zweigenbaum, “Automatic extraction of semantic relations between medical entities: application to the treatment relation,” Journal of Biomedical Semantics, vol. 2, supplement 5, p. S4, 2011.
A. Sharma, R. Swaminathan, and H. Yang, “A verb-centric approach for relationship extraction in biomedical text,” in Proceedings of the 4th IEEE International Conference on Semantic Computing (ICSC ’10), pp. 377–385, IEEE, Pittsburgh, Pa, USA, September 2010.
AbdulWahab Muzaffar, Farooque Azam, and Usman Qamar, A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan Correspondence should be addressed to AbdulWahab Muzaffar; wahab.muz@ceme.nust.edu.pk Received 5 May 2015; Revised 17 June 2015; Accepted 29 June 2015
J.-J. Kim, Z. Zhang, J. C. Park, and S.-K. Ng, “BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature,” Bioinformatics, vol. 22, no. 5, pp. 597–605, 2006.
C. Giuliano, A. Lavelli, L. Romano, andV. Sommarive, “Exploiting shallow linguistic information for relation extraction from biomedical literature,” EACL, vol. 18, pp. 401–408, 2006.
Hakenberg J, Leaman R, Vo NH, Jonnalagadda S, Sullivan R, Miller C, Tari L, Baral C, Gonzalez G: Efficient extraction of protein-protein interactions from full-text articles. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 2010, 7:481-94.
A. Ben Abacha and P. Zweigenbaum, “A hybrid approach for the extraction of semantic relations from medline abstracts,” in 12th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing ’11), Tokyo, Japan, February 2011, vol. 6609 of Lecture Notes in Computer Science, part II, pp. 139–150, Springer, Berlin, Germany, 2011.
Kabiljo R, Clegg AB, Shepherd AJ: A realistic assessment of methods for extracting gene/protein interactions from free text. BMC bioinformatics 2009, 10:233.
Pyysalo S, Airola A, Heimonen J, Björne J, Ginter F, Salakoski T: Comparative analysis of five protein-protein interaction corpora. BMC bioinformatics 2008, 9 Suppl 3:S6
R. A. Kadir and B. Bokharaeian, “Overview of biomedical relations extraction using hybrid rule-based approaches,” Journal of Industrial and Intelligent Information, vol. 1, no. 3, pp. 169–173, 2013.
Miwa M, Sætre R, Miyao Y, Tsujii J: A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2009:121-130.
Ananiadou S, Kell DB, Tsujii J-ichi: Text mining and its potential applications in systems biology. Trends in biotechnology 2006, 24:571-9.
Saetre R, Yoshida K, Miwa M, Matsuzaki T, Kano Y, Tsujii J: Extracting protein interactions from text with the unified AkaneRE event extraction system. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 2010, 7:442-53.
M. Huang, X. Zhu, and M. Li, “A hybrid method for relation extraction from biomedical literature,” International Journal of Medical Informatics, vol. 75, no. 6, pp. 443–455, 2006.
Hakenberg J: Mining Relations from the Biomedical Literature. PhD Thesis 2009:179.ph is placed at the top left of the biography. Personal hobbies will be deleted from the biography.
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of EMNLP 2011, UK, Edinburgh (2011)
Banko, M., Etzioni, O.: The tradeoffs between open and traditional relation extraction. In: Proceedings of ACL-08: HLT, pp. 28–36 (2008)
Bunescu, R., Mooney, R.: Subsequence kernels for relation extraction. In: Weiss, Y., Sch¨olkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems 18, pp. 171–178. MIT Press, Cambridge (2006)
Lewin I, Kafkas S, Rebholz-Schuhmann D (2012) Centroids: Gold standards with distributional variation. LREC pp. 3894–3900.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.