Abstract
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.
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References
Benz, D., Hotho, A., Stumme, G., Stützer, S.: Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge. In: Proceedings of the 2nd Web Science Conference (WebSci10) (2010)
Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Intell. Res. (JAIR) 24(1), 305–339 (2005)
Cruse, D.A.: Hyponymy and its varieties. In: Green, R., Bean, C.A., Myaeng, S.H. (eds.) The Semantics of Relationships: An Interdisciplinary Perspective, pp. 3–21. Springer, Dordrecht (2002). https://doi.org/10.1007/978-94-017-0073-3_1
Dellschaft, K., Staab, S.: Measuring the similiarity of concept hierarchies and its influence on the evaluation of learning procedures. Master’s thesis (Diplomarbeit), University of Koblenz-Landau (2005)
Dellschaft, K., Staab, S.: On how to perform a gold standard based evaluation of ontology learning. 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. 228–241. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_17
Dong, H., Wang, W., Frans, C.: Deriving dynamic knowledge from academic social tagging data: a novel research direction. In: iConference 2017 Proceedings (2017)
García-Silva, A., Corcho, O., Alani, H., Gómez-Pérez, A.: Review of the state of the art: discovering and associating semantics to tags in folksonomies. Knowl. Eng. Rev. 27(1), 57–85 (2012)
Griffiths, T.L., Steyvers, M.: Prediction and semantic association. In: Proceedings of the 15th International Conference on Neural Information Processing Systems, pp. 11–18. MIT Press (2002)
Heymann, P., Garcia-Molina, H.: Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical report, Stanford University (2006)
Jabeen, F., Khusro, S.: Quality-protected folksonomy maintenance approaches: a brief survey. Knowl. Eng. Rev. 30(5), 521–544 (2015)
Maedche, A., Staab, S.: Measuring similarity between ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 251–263. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45810-7_24
Meo, P.D., Quattrone, G., Ursino, D.: Exploitation of semantic relationships and hierarchical data structures to support a user in his annotation and browsing activities in folksonomies. Inf. Syst. 34(6), 511–535 (2009)
Mika, P.: Ontologies are us: a unified model of social networks and semantics. Web Semant.: Sci. Serv. Agents World Wide Web 5(1), 5–15 (2007)
Peters, I., Becker, P.: Folksonomies: Indexing and Retrieval in Web 2.0. De Gruyter/Saur, Berlin (2009)
Stock, W.G.: Concepts and semantic relations in information science. J. Am. Soc. Inf. Sci. Technol. 61(10), 1951–1969 (2010)
Strohmaier, M., Helic, D., Benz, D., Körner, C., Kern, R.: Evaluation of folksonomy induction algorithms. ACM Trans. Intell. Syst. Technol. 3(4), 1–22 (2012)
Tho, Q.T., Hui, S.C., Fong, A.C.M., Cao, T.H.: Automatic fuzzy ontology generation for semantic web. IEEE Trans. Knowl. Data Eng. 18(6), 842–856 (2006)
Wang, W., Barnaghi, P.M., Bargiela, A.: Probabilistic topic models for learning terminological ontologies. IEEE Trans. Knowl. Data Eng. 22(7), 1028–1040 (2010)
Weller, K.: Knowledge Representation in the Social Semantic Web. De Gruyter Saur, Berlin/New York (2010)
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Dong, H., Wang, W., Coenen, F. (2018). Rules for Inducing Hierarchies from Social Tagging Data. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds) Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science(), vol 10766. Springer, Cham. https://doi.org/10.1007/978-3-319-78105-1_38
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