Abstract
This paper explores the issue of detecting concepts for ontology learning from text. Using our tool OntoCmaps, we investigate various metrics from graph theory and propose voting schemes based on these metrics. The idea draws its root in social choice theory, and our objective is to mimic consensus in automatic learning methods and increase the confidence in concept extraction through the identification of the best performing metrics, the comparison of these metrics with standard information retrieval metrics (such as TF-IDF) and the evaluation of various voting schemes. Our results show that three graph-based metrics Degree, Reachability and HITS-hub were the most successful in identifying relevant concepts contained in two gold standard ontologies.
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Adali, S., Hill, B., Magdon-Ismail, M.: The Impact of Ranker Quality on Rank Aggregation Algorithms: Information vs. Robustness. In: Proc. of 22nd Int. Conf. on Data Engineering Workshops. IEEE (2006)
Alba, A., Bhagwan, V., Grace, J., Gruhl, D., Haas, K., Nagarajan, M., Pieper, J., Robson, C., Sahoo, N.: Applications of Voting Theory to Information Mashups. In: IEEE International Conference on Semantic Computing, pp. 10–17 (2008)
Cimiano, P.: Ontology Learning and Population from Text. Algorithms, Evaluation and Applications. Springer (2006)
Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the Web. In: Proc. of the 10th International Conference on WWW, pp. 613–622. ACM (2001)
Endriss, U.: Computational Social Choice: Prospects and Challenges. Procedia Computer Science 7, 68–72 (2011)
Fortuna, B., Grobelnik, M., Mladenic, D.: Semi-automatic Data-driven Ontology Construction System. In: Proc. of the 9th Int. Multi-Conference Information Society, pp. 309–318. Springer (2006)
Frantzi, K.T., Ananiadou, S.: The C/NC value domain independent method for multi-word term extraction. Journal of Natural Language Processing 3(6), 145–180 (1999)
Hatala, M., Gašević, D., Siadaty, M., Jovanović, J., Torniai, C.: Can Educators Develop Ontologies Using Ontology Extraction Tools: an End User Study. In: Proc. 4th Euro. Conf. Technology-Enhanced Learning, pp. 140–153 (2009)
JUNG, http://jung.sourceforge.net/ (last retrieved on December 6, 2011)
Kozareva, Z., Hovy, E.: Insights from Network Structure for Text Mining. In: Proc. of the 49th Annual Meeting of the ACL Human Language Technologies, Portland (2011)
Maedche, A., Staab, S.: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2), 72–79 (2001)
Polikar, R.: Bootstrap inspired techniques in computational intelligence: ensemble of classifiers, incremental learning, data fusion and missing features. IEEE Signal Processing Magazine 24, 59–72 (2007)
Porello, D., Endriss, U.: Ontology Merging as Social Choice. In: Proceedings of the 12th International Workshop on Computational Logic in Multi-agent Systems (2011)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 515–523 (1988)
SCORM (2011), http://www.adlnet.gov (last retrieved on December 10, 2011)
Sculley, D.: Rank Aggregation for Similar Items. In: Proc. of the 7th SIAM International on Data Mining (2007)
Shili, L.: Rank aggregation methods. In: WIREs Comp. Stat. 2010, vol. 2, pp. 555–570 (2010)
Zouaq, A., Gasevic, D., Hatala, M.: Towards Open Ontology Learning and Filtering. Information Systems 36(7), 1064–1081 (2011)
Zouaq, A., Nkambou, R.: Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project. IEEE Trans. on Kdge and Data Eng. 21(11), 1559–1572 (2009)
Zouaq, A.: An Ontological Engineering Approach for the Acquisition and Exploitation of Knowledge in Texts. PhD Thesis, University of Montreal (2008) (in French)
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Zouaq, A., Gasevic, D., Hatala, M. (2012). Voting Theory for Concept Detection. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_28
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DOI: https://doi.org/10.1007/978-3-642-30284-8_28
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