The week's pick
Reseach Article
A Framework for Extracting Biological Relations from Different Resources
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 119 - Number 3 |
| Year of Publication: 2015 |
| Authors: Enas M.f. El Houby |
10.5120/21044-3675
|
Enas M.f. El Houby . A Framework for Extracting Biological Relations from Different Resources. International Journal of Computer Applications. 119, 3 ( June 2015), 1-8. DOI=10.5120/21044-3675
Abstract
The World Wide Web provides a vast source of information of almost all types. Biological data specifically have increased dramatically in the past years because of the exponential growth of knowledge in biological domain. It is very difficult to search for the required data in unstructured documents. Text documents often hide valuable structured data. This data can be exploited if available as a relational table that could be used to answer queries or to perform data mining tasks. Manually extracting biological relations from published literature and transforming them into machine-understandable knowledge is a difficult task because biological domain comprises huge, dynamic, and complicated knowledge. Automatic extraction of semantic relation between biological terms from unstructured documents is challenging in information extraction and important task for deep information processing and management. In this research, a framework has been developed to extract different relations between various biological entities from documents. Semi supervised approach has been used to develop the framework. It requires the user to just provide a handful of valid pairs as initial seeds of the target relation, with no other training. Different patterns can be generated from initial seeds, and then from these patterns additional relation pairs can be extracted. The results has showed that different relations can be extracted such as gene-disease, protein-protein.
References
- Agichtein, E. , & Gravano, L. , Snowball: Extracting relations from large plain-text collections, Proceedings of the Fifth ACM International Conference on Digital Libraries, 2000.
- Brin, S. , "Extracting patterns and relations from the world wide web", WebDB Workshop at 6th International Conference on Extending Database Technology, EDBT '1998.
- Minlie Huang, Xiaoyan Zhu, Shilin Ding, Hao Yu and Ming Li, "ONBIRES: Ontology-based Biological Relation Extraction System", In Proceedings of the Fourth Asia Pacific Bioinformatics Conference, 2006.
- Liu, Y. , Shi, Z. , & Sarkar, A. (2007). Exploiting rich syntactic information for relationship extraction from biomedical articles. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers (pp. 97–100). Rochester, New York: Association for Computational Linguistics.
- D. Fisher, S. Soderland, J. McCarthy, F. Feng, and W. Lehnert. Description of the UMass systems as used for MUC-6. In Proceedings of the 6th Message Understanding Conference. Columbia, MD, 1995.
- O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. -M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates, "WebScale Information Extraction in KnowItAll", ACM 1-58113-844-X/04/0005, May, 2004, New York, USA.
- Hasegawa, T. , Sekine, S. and Grishman, R. "Discovering relations among named entities from large corpora," in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ser. ACL '04. Stroudsburg, PA, USA, (2004).
- D. Yarowsky, "Unsupervised word sense disambiguation rivaling supervised methods", In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189–196. Cambridge, MA, 1995.
- Craven M, Kumlien J, Constructing Biological Knowledge Bases by Extracting Information from Text Sources, Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology 1999.
- Abulaish M, Dey L,"Biological relation extraction and query answering from medline abstracts using ontology- based text mining", Data Knowl Eng, 2007;61(2):228–62.
- Frunza, Oana, Diana Inkpen, and Thomas Tran. "A machine learning approach for identifying disease-treatment relations in short texts. " Knowledge and Data Engineering, IEEE Transactions on 23. 6 (2011): 801-814. ?
- Wen-Juan Hou, Hsiao-Yuan Chen, "Rule extraction in gene–disease relationship discovery", Gene 518 (2013) 132–138.
- Ning Kang, Bharat Singh, Chinh Bui, Zubair Afzal, Erik M van Mulligen and Jan A Kors, "Knowledge-based extraction of adverse drug events from biomedical text. " BMC bioinformatics 15. 1 (2014): 64. ?
- Jump up, Carvalko, J. R. , Preston K, On Determining Optimum Simple Golay Marking Transforms for Binary Image Processing, IEEE Transactions on Computers 21: 1430–33. doi:10. 1109/T-C. 1972. 223519,1972.
- Quan, Changqin, Meng Wang, and Fuji Ren. "An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature. " PloS one 9. 7 (2014): e102039.
- Carlson, Andrew, et al. , 2010. Toward an architecture for never ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 1306–1313.
- Chao Chen, Liang He, Xin Lin, " REV: Extracting Entity Relations from World Wide Web", In proceeding of ACM, 978-1-4503-1172-4, ICUIMC'12, February 20–22, 2012, Kuala Lumpur, Malaysia.
- Xu, Rong, and QuanQiu Wang. "A semi-supervised approach to extract pharmacogenomics-specific drug–gene pairs from biomedical literature for personalized medicine. " Journal of biomedical informatics 46. 4 (2013): 585-593. ?
- Xu, Rong, Li Li, and QuanQiu Wang. "Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature. " Bioinformatics 29. 17 (2013): 2186-2194.
- Xu, Rong, Li Li, and QuanQiu Wang. "dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text. " BMC bioinformatics 15. 1 (2014): 105. ?
- Banko, M. , Cafarella, M. J. , Soderland, S. , Broadhead, M. , & Etzioni, O. (2007). Open information extraction from the web. IJCAI '07: Proceedings of the 20th International Joint Conference on Artificial Intelligence. Hyderabad, India.
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