Abstract
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distributions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.
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
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
https://www.kde.cs.uni-kassel.de/bibsonomy/dumps, the “2015-07-01” version.
- 6.
- 7.
http://downloads.dbpedia.org/2015-10/core/, the “2015-10” version.
References
Almoqhim, F., Millard, D.E., Shadbolt, N.: Improving on popularity as a proxy for generality when building tag hierarchies from folksonomies. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 95–111. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13734-6_7
Andrews, P., Pane, J.: Sense induction in folksonomies: a review. Artif. Intell. Rev. 40(2), 147–174 (2013)
Andrews, P., Pane, J., Zaihrayeu, I.: Semantic disambiguation in folksonomy: a case study. In: Bernardi, R., Chambers, S., Gottfried, B., Segond, F., Zaihrayeu, I. (eds.) AT4DL/NLP4DL -2009. LNCS, vol. 6699, pp. 114–134. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23160-5_8
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 (WebSci 2010) (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J.L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)
Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, New York Inc., Secaucus (2006). https://doi.org/10.1007/978-0-387-39252-3
Djuana, E., Xu, Y., Li, Y.: Learning personalized tag ontology from user tagging information. In: Proceedings of the Tenth Australasian Data Mining Conference - Volume 134 (AusDM 2012), pp. 183–189. Australian Computer Society, Inc. (2012)
Dong, H., Wang, W., Liang, H.N.: Learning structured knowledge from social tagging data: a critical review of methods and techniques. In: 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 307–314, December 2015
Dong, H., Wang, W., Frans, C.: Deriving dynamic knowledge from academic social tagging data: a novel research direction. In: iConference 2017 Proceedings. iSchools (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)
García-Silva, A., García-Castro, L.J., García, A., Corcho, O.: Social tags and linked data for ontology development: A case study in the financial domain. In: The 4th International Conference on Web Intelligence, Mining and Semantics, pp. 1–10. ACM (2014)
Griffiths, T.L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychol. Rev. 114(2), 211 (2007)
Heymann, P., Garcia-Molina, H.: Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical report, Stanford (2006)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006). https://doi.org/10.1007/11762256_31
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (2003)
Isaac, A., Summers, E.: SKOS simple knowledge organization system primer. W3C Working Group Note. World Wide Web Consortium (W3C) (2009). https://www.w3.org/TR/skos-primer/. Accessed 10 July 2018
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)
Niebler, T., Hahn, L., Hotho, A.: Learning word embeddings from tagging data: a methodological comparison. In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, pp. 229–240 (2017)
Osuna, E., Freund, R., Girosi, F.: Support vector machines: training and applications. Technical report, AI Memo 1602, Massachusetts Institute of Technology (1997)
Rêgo, A.S.C., Marinho, L.B., Pires, C.E.S.: A supervised learning approach to detect subsumption relations between tags in folksonomies. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing (SAC 2015), pp. 409–415. ACM (2015)
Steyvers, M., Griffiths, T.: Probabilistic topic models. Handb. Latent Semant. Anal. 427(7), 424–440 (2007)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2005)
Tang, J., Leung, H.f., Luo, Q., Chen, D., Gong, J.: Towards ontology learning from folksonomies. In: Proceedings of the IJCAI, vol. 9, pp. 2089–2094 (2009)
Vander Wal, T.: Folksonomy (2007). http://vanderwal.net/folksonomy.html. Accessed 07 June 2018
Veropoulos, K., Campbell, C., Cristianini, N., et al.: Controlling the sensitivity of support vector machines. In: Proceedings of the IJCAI, pp. 55–60 (1999)
Wang, W., Barnaghi, P.M., Bargiela, A.: Probabilistic topic models for learning terminological ontologies. IEEE Trans. Knowl. Data Eng. 22(7), 1028–1040 (2010)
Wetzker, R., Zimmermann, C., Bauckhage, C.: Analyzing social bookmarking systems: A del.icio.us cookbook. In: Proceedings of the ECAI 2008 Mining Social Data Workshop, pp. 26–30 (2008)
Zhou, M., Bao, S., Wu, X., Yu, Y.: An unsupervised model for exploring hierarchical semantics from social annotations. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 680–693. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_49
Acknowledgment
This research is funded by the Research Development Fund at Xi’an Jiaotong-Liverpool University, contract number RDF-10-2015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Dong, H., Wang, W., Coenen, F. (2018). Learning Relations from Social Tagging Data. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-97304-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97303-6
Online ISBN: 978-3-319-97304-3
eBook Packages: Computer ScienceComputer Science (R0)