Abstract
Nowadays, ontologies are considered an important tool for knowledge structuring and reusing, especially in domains in which the proper organization and processing of information are critical issues (e.g. biomedicine). In these domains, the number of available ontologies has grown rapidly during the last years. This is very positive because it enables a more effective (or more intelligent) knowledge management. However, it raises a new problem: what ontology should be used for a given task? In this work, an approach for the automatic recommendation of ontologies is proposed. This approach is based on measuring the adequacy of an ontology to a given context according to three independent criteria: (i) the extent to which the ontology covers the context, (ii) the semantic richness of the ontology in the context, and (iii) the popularity of the ontology in the Web 2.0. Results show the importance of using collective knowledge in the fields of ontology evaluation and recommendation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sabou, M., Lopez, V., Motta, E., Uren, V.: Ontology Selection: Ontology Evaluation on the Real Semantic Web. In: Evaluation of Ontologies on the Web Workshop, Held in Conjunction with WWW 2006, Edinburgh, Scotland (2006)
Gómez-Pérez, A.: Some Ideas and Examples to Evaluate Ontologies. In: 11th IEEE Conference on Artificial Intelligence Applications, pp. 299–305. IEEE Computer Society Press, Los Angeles (1995)
Gómez-Pérez, A.: From Knowledge Based Systems to Knowledge Sharing Technology. In: Evaluation and Assessment. KSL Lab, Stanford University, CA (1994)
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic Web. Scientific American 284(5), 34–43 (2001)
Supekar, K., Patel, C., Lee, Y.: Characterizing Quality of Knowledge on Semantic Web. In: Seventeenth International FLAIRS Conference, Miami, Florida, USA, pp. 220–228 (2004)
Alani, H., Noy, N., Shah, N., Shadbolt, N., Musen, M.: Searching Ontologies Based on Content: Experiments in the Biomedical Domain. In: Fourth International Conference on Knowledge Capture (K-Cap), Whistler, BC, Canada, pp. 55–62. ACM Press (2007)
Alani, H., Brewster, C., Shadbolt, N.R.: Ranking Ontologies with aKTiveRank. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 1–15. Springer, Heidelberg (2006)
Netzer, Y., Gabay, D., Adler, M., Goldberg, Y., Elhadad, M.: Ontology Evaluation through Text Classification. In: Chen, L., Liu, C., Zhang, X., Wang, S., Strasunskas, D., Tomassen, S.L., Rao, J., Li, W.-S., Candan, K.S., Chiu, D.K.W., Zhuang, Y., Ellis, C.A., Kim, K.-H. (eds.) WCMT 2009. LNCS, vol. 5731, pp. 210–221. Springer, Heidelberg (2009)
Vilches-Blázquez, L., Ramos, J., López-Pellicer, F., Corcho, O., Nogueras-Iso, J.: An Approach to Comparing Different Ontologies in the Context of Hydrographical Information. In: Heidelberg, S.B. (ed.) Information Fusion and Geographic Information Systems. Lecture Notes in Geoinformation and Cartography, vol. 4, pp. 193–207. Springer, Berlin (2009)
Jonquet, C., Musen, M., Shah, N.: Building a Biomedical Ontology Recommender Web Service. Journal of Biomedical Semantics (S1), 1–18 (2010)
Sabou, M., Lopez, V., Motta, E.: Ontology Selection for the Real Semantic Web: How to Cover the Queen’s Birthday Dinner? In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 96–111. Springer, Heidelberg (2006)
Brank, J., Grobelnik, M., Mladenic, D.A.: Survey of Ontology Evaluation Techniques. In: Conference on Data Mining and Data Warehouses (SiKDD 2005), Ljubljana, Slovenia (2005)
Jones, M., Alani, H.: Content-based ontology ranking. In: 9th Int. Protégé Conference, Stanford, CA (2006)
Romero, M.M., Vázquez -Naya, J.M., Munteanu, C.R., Pereira, J., Pazos, A.: An Approach for the Automatic Recommendation of Ontologies Using Collaborative Knowledge. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6277, pp. 74–81. Springer, Heidelberg (2010)
Studer, R., Benjamins, V.R., Fensel, D.: Knowledge Engineering: Principles and Methods. IEEE Transactions on Data & Knowledge Engineering 25(1-2), 161–197 (1998)
Liu, H., Hussain, F., Tan, C., Dash, M.: Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6(4), 393–423 (2002)
Daniel, W., Wayne, W.: Biostatistics: a Foundation for Analysis in the Health Sciences, 9th edn. John Wiley and Sons, New York (2009)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Martínez-Romero, M., Vázquez-Naya, J.M., Pereira, J., Pazos, A. (2012). A Multi-criteria Approach for Automatic Ontology Recommendation Using Collective Knowledge. In: Recommender Systems for the Social Web. Intelligent Systems Reference Library, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25694-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-25694-3_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25693-6
Online ISBN: 978-3-642-25694-3
eBook Packages: EngineeringEngineering (R0)