Abstract
Within powerful social web interactions, we have witnessed an explosive growth of shared documents on the web. Indeed, the social web has been scaled up with massive shared web resources annotated by ordinary folks. The collection of folks’ tags creates a folksonomy. This collaborative tagging system enables an open exploration of each user’s tags describing web resources. Despite its simplicity of organizing web resources, it rises up ambiguous and inconsistent tags that semantically weaken the description of web resources’ content. To achieve an enriched and structured map of knowledge, it is essential to optimally retrieve organized web resources through pertinently describing them with relevant descriptors “metadata”. This article represents a combined semantic enrichment strategy using collaborative tagging guided by ontology towards pertinently describe web resources. In fact, relevant measures of performances attest the efficiency of our proposal that explores relevant folksonomy’s tags to extract web resources’ content main keywords and retrieve matching terms from a defined lightweight ontology. The alignment of social labeling with the ontology’s formalism will implicitly build an emergent semantic of enriched web resources that will establish new challenges to improve context-aware recommender systems of web resources.
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
References
Lau Raymond, Y.K., Leon Zhao, J., Wenping, Z., Yi, C., Ngai Eric, W.T.: Learning context-sensitive domain ontologies from folksonomies: a cognitively motivated method. INFORMS J. Comput. 27(3), 561–578 (2015). doi:10.1287/ijoc.2015.0644
Qassimi, S., Abdelwahed, E.H., Hafidi, M., Lamrani, R.: Enrichment of ontology by exploiting collaborative tagging systems: a contextual semantic approach. In: Third International Conference on Systems of Collaboration (SysCo), IEEE Conference Publications, pp. 1–6 (2016)
Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z., Jain, L.C.: Folksonomy and tag-based recommender systems in e-learning environments. E-Learning Systems. ISRL, vol. 112, pp. 77–112. Springer, Cham (2017). doi:10.1007/978-3-319-41163-7_7
Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L., Jschke, R., Hotho, A., Stumme, G.: Social tagging recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 615–644. Springer, Berlin (2011). doi:10.1007/978-0-387-85820-3_19
Špiraneca, S., Ivanjko, T.: Experts vs. novices tagging behavior: an exploratory analysis. Procedia - Soc. Behav. Sci. 73, 456–459 (2013)
Jean-Louis, L., Zouaq, A., Gagnon, M., Ensan, F.: An assessment of online semantic annotators for the keyword extraction task. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 548–560. Springer, Cham (2014). doi:10.1007/978-3-319-13560-1_44
Thomas, J.R., Bharti, S.K., Babu, K.S.: Automatic keyword extraction for text summarization in e-newspapers. In: Proceedings of the International Conference on Informatics and Analytics, pp. 86–93. ACM (2016)
Turney, P.D.: Learning to extract keyphrases from text. Technical report ERB-1057, National Research Council Canada, Institute for Information Technology (1999)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: practical automatic keyphrase extraction. In: Proceedings of ACM Conference on Digital Libraries, Berkeley, CA, US, pp. 254–255. ACM Press, New York (1999)
El-Beltagy, S.R., Rafea, A.: KP-miner: a keyphrase extraction system for English and Arabic documents. Inf. Syst. 34(1), 132–144 (2009)
Budura, A., Michel, S., Cudre-Mauroux, P., Aberer, K.: To tag or not to tag - harvesting adjacent metadata in large-scale tagging systems. In: Proceedings International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, pp. 733–734. ACM Press, New York (2008)
Hassan, M.M., Karray, F., Kamel, M.S.: Automatic document topic identification using Wikipedia hierarchical ontology. In: Proceedings of the Eleventh IEEE International Conference on Information Science, Signal Processing and their Applications, pp. 237–242 (2012)
Allahyari, M., Kochut, K.: Semantic tagging using topic models exploiting Wikipedia category network. In: Proceedings of the 10th International Conference on Semantic Computing (2016)
Fang, Q., Xu, C., Jitao, S., Shamim Hossain, M., Ghoneim, A.: Folksonomy-based visual ontology construction and its applications. IEEE Trans. Multimedia 18(4), 702–713 (2016)
SKOS Simple Knowledge Organization System. https://www.w3.org/TR/skos-reference/
Lovins, J.B.: Development of a stemming algorithm. Mech. Trans. Comput. Linguist. 11(1–2), 11–31 (1968)
Maui - Multi-purpose automatic topic indexing. http://www.medelyan.com/software
Fu, W.-T., Kannampallil, T., Kang, R., He, J.: Semantic imitation in social tagging. ACM Trans. Comput.-Hum. Interact. 17(3), 1–37 (2010)
US National Library of Medicine National Institutes of Health: Medical Subject Headings (MeSH). https://www.nlm.nih.gov/mesh
CiteULike. http://www.citeulike.org/
Chuang, H.-Y., Lee, E., Liu, Y.-T., Lee, D., Ideker, T.: Network-based classification of breast cancer metastasis (2007). doi:10.1038/msb4100180
Naderi, A., Teschendorff, A.E., Barbosa-Morais, N.L., Pinder, S.E., Green, A.R., Powe, D.G., Robertson, J.F.R., Aparicio, S., Ellis, I.O., Brenton, J.D., Caldas, C.: A gene-expression signature to predict survival in breast cancer across independent data sets (2007). doi:10.1038/sj.onc.1209920
RAKE Homepage. https://hackage.haskell.org/package/rake
Vrije Universiteit Amsterdam, MeSH terms Homepage. http://libguides.vu.nl/PMroadmap/MeSH
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. Mcgraw Hill Computer Science Series (1983)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Qassimi, S., Abdelwahed, E.H., Hafidi, M., Lamrani, R. (2017). Towards an Emergent Semantic of Web Resources Using Collaborative Tagging. In: Ouhammou, Y., Ivanovic, M., Abelló, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2017. Lecture Notes in Computer Science(), vol 10563. Springer, Cham. https://doi.org/10.1007/978-3-319-66854-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-66854-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66853-6
Online ISBN: 978-3-319-66854-3
eBook Packages: Computer ScienceComputer Science (R0)