Abstract
We present a new method to evaluate a search ontology, which relies on mapping ontology instances to textual documents. On the basis of this mapping, we evaluate the adequacy of ontology relations by measuring their classification potential over the textual documents. This data-driven method provides concrete feedback to ontology maintainers and a quantitative estimation of the functional adequacy of the ontology relations towards search experience improvement. We specifically evaluate whether an ontology relation can help a semantic search engine support exploratory search.
We test this ontology evaluation method on an ontology in the Movies domain, that has been acquired semi-automatically from the integration of multiple semi-structured and textual data sources (e.g., IMDb and Wikipedia). We automatically construct a domain corpus from a set of movie instances by crawling the Web for movie reviews (both professional and user reviews). The 1-1 relation between textual documents (reviews) and movie instances in the ontology enables us to translate ontology relations into text classes. We verify that the text classifiers induced by key ontology relations (genre, keywords, actors) achieve high performance and exploit the properties of the learned text classifiers to provide concrete feedback on the ontology.
The proposed ontology evaluation method is general and relies on the possibility to automatically align textual documents to ontology instances.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Burkhardt, F., Gulla, J.A., Liu, J., Weiss, C., Zhou, J.: Semi automatic ontology engineering in business applications. In: Proceedings of the 3rd International AST Workshop – Applications of Semantic Technologies. LNI, vol. 134, pp. 688–693 (2008)
Baeza-Yates, R., Ciaramita, M., Mika, P., Zaragoza, H.: Towards semantic search. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 4–11. Springer, Heidelberg (2008)
Gulla, J.A., Borch, H.O., Ingvaldsen, J.E.: Ontology learning for search applications. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part I. LNCS, vol. 4803, pp. 1050–1062. Springer, Heidelberg (2007)
Gangemi, A., Catenacci, C., Ciaramita, M., Lehmann, J.: Modelling ontology evaluation and validation. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 140–154. Springer, Heidelberg (2006)
Gomez-Perez, A.: Evaluation of ontologies. International Journal of Intelligent Systems 16, 391–409 (2001)
Alani, H., Brewster, C.: Ontology ranking based on the analysis of concept sructures. In: Proceedings of the 3rd International Conference on Knowledge Capture (K-Cap), Banff, Canada, pp. 51–58 (2005)
JupiterResearch: Search technology buyerś guide. Technical report, IBM Content Discovery (2006), ftp://ftp.software.ibm.com/software/data/cmgr/pdf/searchbuyersguide.pdf
Aula, A.: Query formulation in web information search. In: Proceedings of IADIS International Conference WWW/Internet, pp. 403–410 (2003)
Strasunskas, D., Tomassen, S.L.: Empirical insights on a value of ontology quality in ontology-driven web search. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1319–1337. Springer, Heidelberg (2008)
Tartir, S., Arpinar, I., Moore, M., Sheth, A., Aleman-Meza, B.: OntoQA: Metric-based ontology quality analysis. In: Proceedings of Workshop on Knowledge Acquisition, Autonomous, Semantically Heterogeneous Data and Knowledge Sources, pp. 45–53 (2005)
White, R.W., Muresan, G., Marchionini, G. (eds.): ACM SIGIR Workshop on Evaluating Exploratory Search Systems, Seattle (2006)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the Seventh international Conference on Information and Knowledge Management, Bethesda, Maryland, pp. 2–7 (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Akbani, R., Kwek, S.S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Netzer, Y., Gabay, D., Adler, M., Goldberg, Y., Elhadad, M. (2009). Ontology Evaluation through Text Classification. In: Chen, L., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03996-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-03996-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03995-9
Online ISBN: 978-3-642-03996-6
eBook Packages: Computer ScienceComputer Science (R0)