Abstract
Construction of Ontology is indispensable with rapid increase in textual information. Much research in learning Ontology are supervised and require manually annotated resources. Also, quality of Ontology is dependent on quality of corpus which may not be readily available. To tackle these problems, we present an iterative focused web crawler for building corpus and an unsupervised framework for construction of Domain Ontology. The proposed framework consists of five phases, Corpus Collection using Iterative Focused crawling with novel weighting measure, Term Extraction using HITS algorithm, Taxonomic Relation Extraction using Hearst and Morpho-Syntactic Patterns, Non Taxonomic relation extraction using association rule mining and Domain Ontology Building. Evaluation results show that proposed crawler outweighs traditional crawling techniques, domain terms showed higher precision when compared to statistical techniques and learnt ontology has rich knowledge representation.
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References
Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: An overview of graph-based keyword extraction methods and approaches. J. Inf. Organ. Sci. 39(1), 1–20 (2015)
De Knijff, J., Frasincar, F., Hogenboom, F.: Domain taxonomy learning from text: the subsumption method versus hierarchical clustering. Data Knowl. Eng. 83, 54–69 (2013)
Drymonas, E., Zervanou, K., Petrakis, E.G.M.: Unsupervised ontology acquisition from plain texts: the OntoGain system. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds.) NLDB 2010. LNCS, vol. 6177, pp. 277–287. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13881-2_29
Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)
Liu, L., Peng, T., Zuo, W.: Topical web crawling for domain-specific resource discovery enhanced by selectively using link-context. Proc. Int. Arab J. Inf. Technol. 12(2), 196–204 (2015)
Lopez, V., Pasin, M., Motta, E.: AquaLog: an ontology-portable question answering system for the semantic web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 546–562. Springer, Heidelberg (2005). doi:10.1007/11431053_37
Lossio-Ventura, J.A., Jonquet, C., Roche, M., Teisseire, M.: Yet another ranking function for automatic multiword term extraction. In: Przepiórkowski, A., Ogrodniczuk, M. (eds.) NLP 2014. LNCS (LNAI), vol. 8686, pp. 52–64. Springer, Cham (2014). doi:10.1007/978-3-319-10888-9_6
Meijer, K., Frasincar, F., Hogenboom, F.: A semantic approach for extracting domain taxonomies from text. Decis. Support Syst. 62, 78–93 (2014)
Mukherjee, S., Ajmera, J., Joshi, S.: Domain cartridge: unsupervised framework for shallow domain ontology construction from corpus. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 929–938. ACM (2014)
Nabila, N., Mamat, A., Azmi-Murad, M., Mustapha, N.: Enriching non-taxonomic relations extracted from domain texts. In: 2011 International Conference on Semantic Technology and Information Retrieval, pp. 99–105. IEEE (2011)
Ochoa, J.L., Almela, Á., Hernández-Alcaraz, M.L., Valencia-García, R.: Learning morphosyntactic patterns for multiword term extraction. Sci. Res. Essays 6(26), 5563–5578 (2011)
Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., Mladenic, D.: Triplet extraction from sentences. In: Proceedings of the 10th International Multiconference Information Society-IS, pp. 8–12 (2007)
Serra, I., Girardi, R.: A process for extracting non-taxonomic relationships of ontologies from text (2011)
Gangly, B., Sheikh, R.: A review of focused web crawling strategies. Int. J. Adv. Comput. Res. 2(4) (2012)
Shue, L.Y., Chen, C.W., Shiue, W.: The development of an ontology-based expert system for corporate financial rating. Expert Syst. Appl. 36(2), 2130–2142 (2009)
Srikant, R., Agrawal, R.: Mining generalized association rules. IBM Research Division (1995)
Sure, Y., Staab, S., Studer, R.: Ontology engineering methodology. In: Staab, R., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems, pp. 135–152. Springer, Heidelberg (2009). doi:10.1007/978-3-540-92673-3_6
Tartir, S., Arpinar, I.B., Moore, M., Sheth, A.P., Aleman-Meza, B.: Ontoqa: metric-based ontology quality analysis (2005)
Thenmalar, S., Geetha, T.: The modified concept based focused crawling using ontology. J. Web Eng. 13(5–6), 525–538 (2014)
Uzun, Y.: Keyword extraction using naïve bayes. Bilkent University, Department of Computer Science, Turkey (2005). www.cs.bilkent.edu.tr/~guvenir/courses/CS550/Workshop/Yasin_Uzun.pdf
Zhang, Y., Vasconcelos, W., Sleeman, D.: Ontosearch: an ontology search engine. In: Bramer, M., Coenen, F., Allen, T. (eds.) Research and Development in Intelligent Systems XXI. Springer, London (2005). doi:10.1007/1-84628-102-4_5
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Venu, S.H., Mohan, V., Urkalan, K., T.V., G. (2017). Unsupervised Domain Ontology Learning from Text. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_13
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DOI: https://doi.org/10.1007/978-3-319-58130-9_13
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