AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

A Semantic Retrieval System for Extracting Relationships from Biological Corpus


Affiliations
1 Department of Information Systems, Benha University, Egypt
 

The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.

Keywords

Inverted List, Information Retrieval, Gene Ontology, Information Extraction, Relationship Extraction, Pattern Recognition.
User
Notifications
Font Size

  • Ramani AK, Bunescu RC, Mooney RJ, Marcotte EM, Consolidating the set of known human proteinprotein interactions in preparation for large-scale mapping of the human interactome, Genome Biol , 6(5), 2001.

  • Ono T, Hishigaki H, Tanigami A, Takagi T, Automated extraction of information on protein-protein interactions from the biological literature, Bioinformatics , 17(2):155-161, 2001.

  • Bunescu RC, Mooney RJ, Subsequence Kernels for Relation Extraction, Proceedings of the 19th Conference on Neural Information Processing Systems, 2005.

  • Blaschke C, Andrade MA, Ouzounis C, Valencia A, Automatic extraction of biological information from scientific text: protein-protein interactions, ProcIntConfIntellSystMolBiol , 60-67, 1999.

  • Rosario B, Hearst A, Multi-way Relation Classification: Application to Protein-Protein Interaction, Human Language Technology Conference on Empirical Methods in Natural Language Processing, 2005.

  • Bishop, Christopher M., Pattern Recognition and Machine Learning, Springer. p. vii, 2006.

  • 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/TC.

  • 223519,1972.

  • Dr AK Sharma. "ontology engine for search." Journal of Computer Applications, (2010).

  • Hu, Meng, and Jiong Yang. "A System of User-Guided Biological Literature Search Engine." IEEE Data Eng. Bull. 28.4,2005

  • Silvestri, Fabrizio, RaffaelePerego, and Salvatore Orlando. "Assigning document identifiers to enhance compressibility of web search engines indexes." Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004.

  • Huang, M., Zhu, X., Ding, S., Yu, H., & Li, M. (2006, February). ONBRIRES: Ontology-Based Biological Relation Extraction System. In APBC (pp. 327-336), 2006.

  • Mangla, Neha, and Vinod Jain. "Context based Indexing in Information Retrieval System using BST."

  • Nebhi, Kamel. "Ontology-based information extraction from twitter." (2012) [14] Buscaldi, Davide, and HaïfaZargayouna. "Yasemir: Yet another semantic information retrieval system." Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval. ACM, 2013.

  • Mihalcea, Rada F., and Silvana I. Mihalcea. "Word semantics for information retrieval: moving one step closer to the Semantic Web." Tools with Artificial Intelligence, Proceedings of the 13th International Conference on. IEEE, 2001.

  • EL-SALAM, Shimaa M. Abd, et al. Extracting Arabic Relations from the Web. arXiv preprint arXiv:1603.02488, 2016.


Abstract Views: 355

PDF Views: 151




  • A Semantic Retrieval System for Extracting Relationships from Biological Corpus

Abstract Views: 355  |  PDF Views: 151

Authors

Hassan Mahmoud
Department of Information Systems, Benha University, Egypt
Saif Salah Kareem
Department of Information Systems, Benha University, Egypt
El-Shishtawy Tarek
Department of Information Systems, Benha University, Egypt

Abstract


The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.

Keywords


Inverted List, Information Retrieval, Gene Ontology, Information Extraction, Relationship Extraction, Pattern Recognition.

References