Skip to main content
SpringerLink
Log in

The role of collaborative tagging and ontologies in emerging semantic of web resources

  • Published:
Computing Aims and scope Submit manuscript

Abstract

The social web interactions have extended the sharing and the growth of web resources on the web. The collaborative web services (folksonomies) enable users to assign their freely chosen keywords (tags) to describe web resources. The advent of folksonomy has evolved the role of web users from consumers to contributors of information. Thus, users attribute their descriptive tags to annotate, organize and classify web resources of interests. Folksonomy became popular with the emergence of collaborative tagging. It offers a practical classification of web resources via the attributed tags. Nonetheless, the freely chosen tags weaken the semantic description of web resources. Folksonomy can give rise to a poor classification system based on ambiguous and inconsistent tags. Therefore, it is essential to pertinently describe the semantic of web resources to enhance their classification, findability and discoverability. The proposed approach represents a combined semantic enrichment strategy that explores collaborative tagging towards describing each web resource using different types of descriptive metadata, namely relevant folksonomy tags, content-based main keywords and matching ontology terms. The experimental evaluation has shown relevant results attesting the efficiency of our proposal. The alignment of social tagging with the ontology will not only enhances the classification of web resources but also constructs their semantic clustering. This emergent semantic will establish new challenges to improve the context-aware recommender systems of web resources in different real-world applications (healthcare, social education and cultural heritage).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Baker M (2013) Every page is page one. XML Press. Laguna Hills. ISBN 978-1937434281

  2. Kang J-H, Lerman K (2011) Leveraging user diversity to harvest knowledge on the social web. In: Proceedings of the IEEE third international conference on social computing (SocialCom)

  3. Lau Raymond YK, Leon Zhao J, Wenping Z, Yi C, Ngai Eric WT (2015) Learning contect-sensitive domain ontologies from folksonomies: a cognitively motivated method. Inf J Comput 27:561–578

    Article  Google Scholar 

  4. Daglas S, Kakali C, Kakavoulis D, Koumaki M, Papatheodorou C (2012) A methodology for folksonomy evaluation. In: Zaphiris P, Buchanan G, Rasmussen E, Loizides F (eds) Theory and practice of digital libraries. Lecture notes in computer science, vol 7489. Springer, Berlin

    Google Scholar 

  5. Kumar KPK, Srivastava A, Geethakumari G (2016) A psychometric analysis of information propagation in online social networks using latent trait theory. Computing 98:583. https://doi.org/10.1007/s00607-015-0472-7

    Article  MathSciNet  Google Scholar 

  6. Feicheng M, Yating L (2014) Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags. Online Inf Rev 38(2):232–247

    Article  Google Scholar 

  7. Khan Minhas MF, Abbasi RA, Aljohani NR, Albeshri AA, Mushtaq M (2015) Intweems: a framework for incremental clustering of tweet streams. In: Proceedings of the 17th international conference on information integration and web-based applications and services, iiWAS 15. ACM, New York, NY, USA, pp 87:1–87:4

  8. Godoy D, Corbellini A (2016) Folksonomy-based recommender systems: a state-of-the-art review. Int J Intell Syst 31(4):314–346. https://doi.org/10.1002/int.21753

    Article  Google Scholar 

  9. Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  10. Sanchez Bocanegra CL, Sevillano Ramos JL, Rizo C, Civit A, Fernandez-Luque L (2017) HealthRecSys: a semantic content-based recommender system to complement health videos. BMC Med Inform Decis Mak 17:63. https://doi.org/10.1186/s12911-017-0431-7

    Article  Google Scholar 

  11. Klašnja-Milićević A, Ivanović M, Vesin B et al (2017) Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Appl Intell. https://doi.org/10.1007/s10489-017-1051-8

    Article  Google Scholar 

  12. Bao J, Zheng Y, Wilkie D et al (2015) Recommendations in location-based social networks: a survey. Geoinformatica 19:525. https://doi.org/10.1007/s10707-014-0220-8

    Article  Google Scholar 

  13. Qassimi S, Abdelwahed EH, 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

    Google Scholar 

  14. Farnan JM, Snyder SL, Worster BK et al (2013) Online medical professionalism: patient and public relationships: policy statement from the American college of physicians and the federation of state medical boards. Ann Intern Med 158(8):620–627

    Article  Google Scholar 

  15. Househ M (2013) The use of social media in healthcare: organizational, clinical, and patient perspectives. Stud Health Technol Inform 183:244–248

    Google Scholar 

  16. Ventola CL (2014) Social media and health care professionals: benefits, risks, and best practices. Pharm Ther 39(7):491–499

    Google Scholar 

  17. Villegas NM, Sánchez C, Díaz-Cely J, Tamura G (2018) Characterizing context-aware recommender systems: a systematic literature review. Knowl Based Syst 140:173–200. https://doi.org/10.1016/j.knosys.2017.11.003

    Article  Google Scholar 

  18. Cao Y, Kovachev D, Klamma R, Jarke M, Lau RW (2015) Tagging diversity in personal learning environments. J Comput Educ 2(1):93–121

    Article  Google Scholar 

  19. Klašnja-Milićević A, Vesin B, Ivanović M, Budimac Z, Jain LC (2017) Folksonomy and tag-based recommender systems in e-learning environments. In: E-learning systems. Intelligent systems reference library, vol 112. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-41163-7_7

    Google Scholar 

  20. Jean-Louis L, Zouaq A, Gagnon M, Ensan F (2014) An assessment of online semantic annotators for the keyword extraction task. In: Pham DN, Park SB (eds) PRICAI 2014: trends in artificial intelligence. PRICAI 2014. Lecture Notes in Computer Science, vol 8862. Springer, Cham, pp 548–560. https://doi.org/10.1007/978-3-319-13560-1_44

    Google Scholar 

  21. Thomas J R, Bharti SK, Babu KS (2016) Automatic keyword extraction for text summarization in e-newspapers. In: Proceedings of the international conference on informatics and analytics, pp 86-93. ACM

  22. Turney PD (1999) Learning to extract keyphrases from text. Technical report ERB-1057, National Research Council Canada, Institute for Information technology

  23. Witten IH, Paynter GW, Frank E, Gutwin C, Nevill-Manning CG (1999) Kea: practical automatic keyphrase extraction. In Proceedings of the ACM conference on digital libraries, Berkeley, CA, US. ACM Press, New York, NY, pp 254–255

  24. Sarkar K (2013) A hybrid approach to extract keyphrases from medical documents. Int J Comput Appl 63(18):14–19. https://doi.org/10.5120/10565-5528

    Article  Google Scholar 

  25. Krapivin M, Autayeu M, Marchese M, Blanzieri E, Segata N (2010) Improving machine learning approaches for keyphrases extraction from scientific documents with natural language knowledge. In: Proceedings of the joint JCDL/ICADL international digital libraries conference. Gold Coast, Australia, pp 102–111

  26. El-Beltagy SR, Rafea A (2009) Kp-miner: a keyphrase extraction system for English and Arabic documents. Inf Syst 34:132–144

    Article  Google Scholar 

  27. Marinho LB, Nanopoulos A, Schmidt-Thieme L, Jäschke R, Hotho A, Stumme G (2011) Social tagging recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Boston, MA, pp 615–644. https://doi.org/10.1007/978-0-387-85820-3_19

    Google Scholar 

  28. Špiraneca S, Ivanjkob T (2013) Experts vs. novices tagging behavior: an exploratory analysis. Procedia Soc Behav Sci 73:456–459

    Article  Google Scholar 

  29. Consortium GO et al (2017) Expansion of the gene ontology knowledgebase and resources. Nucl Acids Res 45(D1):D331–D338

    Article  Google Scholar 

  30. Chen J, Zheng J, Yu H (2016) Finding important terms for patients in their electronic health records: a learning-to-rank approach using expert annotations. JMIR Med Inform 4(4):e40. https://doi.org/10.2196/medinform.6373

    Article  Google Scholar 

  31. Hassan MM, Karray F, Kamel MS (2012) 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

  32. Allahyari M, Kochut K (2016) Semantic tagging using topic models exploiting wikipedia category network. In: Proceedings of the 10th international conference on semantic computing

  33. Osman T, Thakker D, Schaefer G (2014) Utilising semantic technologies for intelligent indexing and retrieval of digital images. Computing 96(7):651–668

    Article  Google Scholar 

  34. Gao G, Liu Y-S, Lin P, Wang M, Gu M, Yong J-H (2017) BIMTag: concept-based automatic semantic annotation of online BIM product resources. Adv Eng Inform 31:48–61

    Article  Google Scholar 

  35. Zubiaga A, Fresno V, Martinez R, Garcia-Plaza AP (2013) Harnessing folksonomies to produce a social classification of resources. IEEE Trans Knowl Data Eng 25(8):1801–1813

    Article  Google Scholar 

  36. Xie Q, Xiong F, Han T et al (2018) Interactive resource recommendation algorithm based on tag information. World Wide Web. https://doi.org/10.1007/s11280-018-0532-y

    Article  Google Scholar 

  37. Qassimi S, Abdelwahed EH, Hafidi M, Lamrani R (2016) 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

  38. Tommasel A, Godoy D (2015) Semantic grounding of social annotations for enhancing resource classification in folksonomies. J Intell Inf Syst 44(3):415–446. https://doi.org/10.1007/s10844-014-0339-y

    Article  Google Scholar 

  39. Yu H, Zhou B, Deng M et al (2017) Tag recommendation method in folksonomy based on user tagging status. J Intell Inf Syst. https://doi.org/10.1007/s10844-017-0468-1

    Article  Google Scholar 

  40. Belém FM, Martins EF, Almeida JM, Goncalves MA (2014) Personalized and object-centered tag recommendation methods for web 2.0 applications. Inf Process Manag 50(4):524–553

    Article  Google Scholar 

  41. Fang Q, Xu Ch, Jitao S, Shamim Hossain M, Ghoneim A (2016) Folksonomy-based visual ontology construction and its applications. IEEE Trans Multimed 18(4):702–713

    Article  Google Scholar 

  42. Maui—multi-purpose automatic topic indexing, Homepage. http://www.medelyan.com/software. Accessed 16 Mar 2018

  43. Duwairi R, Hedaya M (2016) Automatic keyphrase extraction for arabic news documents based on kea system. J Intell Fuzzy Syst 30(4):2101–2110

    Article  Google Scholar 

  44. Lovins JB (1968) Development of a stemming algorithm. Mech Transl Comput Linguist 11(1–2):11–31

    Google Scholar 

  45. Jabeen F, Khusro S (2015) Quality-protected folksonomy maintenance approaches: a brief survey. Knowl Eng Rev 30(5):521–544. https://doi.org/10.1017/S0269888915000120

    Article  Google Scholar 

  46. Kang J, Lerman K (2011) Leveraging user diversity to harvest knowledge on the social web.In: Privacy, Security, Risk and trust (PASSAT) and 2011 IEEE 3rd international conference on social computing (SocialCom), pp 215–222

  47. Papadopoulos S, Vakali A, Kompatsiaris Y (2011) Community detection in collaborative tagging systems. Community-built databases. Springer, Berlin, pp 107–131

    Google Scholar 

  48. SKOS simple knowledge organization system. https://www.w3.org/TR/skos-reference/. Accessed 16 Mar 2018

  49. Nandipati A (2011) Assessment of metadata associated with geotag pictures. Masters thesis, University of Muenster

  50. Zhang L, Tang J, Zhang M (2012) Integrating temporal usage pattern into personalized tag prediction. In: Sheng QZ, Wang G, Jensen CS, Xu G (eds) Web technologies and applications. LNCS 7235. Springer, Berlin, pp 354–365

    Chapter  Google Scholar 

  51. Fu W-T, Kannampallil T, Kang R, He J (2010) Semantic imitation in social tagging. ACM Trans Comput Hum Interact 17(3):1–37

    Article  Google Scholar 

  52. citeulike homepage. http://www.citeulike.org/. Accessed 16 Mar 2018

  53. US National Library of Medicine National Institutes of Health: Medical Subject Headings (MeSH). https://www.nlm.nih.gov/mesh. Accessed 16 Mar 2018

  54. Chuang H-Y et al (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3:140. https://doi.org/10.1038/msb4100180

    Article  Google Scholar 

  55. Naderi A, Teschendorff AE, Barbosa-Morais NL, Pinder SE, Green AR, Powe DG, Robertson JF, Aparicio S, Ellis IO, Brenton JD, Caldas C (2007) A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene 26:1507–1516. https://doi.org/10.1038/sj.onc.1209920

    Article  Google Scholar 

  56. RAKE Homepage. https://hackage.haskell.org/package/rake. Accessed 16 Mar 2018

  57. van Rijsbergen CJ (1979) Information retrieval. Butterworths, London

    MATH  Google Scholar 

  58. Vrije Universiteit Amsterdam, MeSH terms Homepage. http://libguides.vu.nl/PMroadmap/MeSH. Accessed 16 Mar 2018

  59. Musto C, Basile P, Lops P, de Gemmis M, Semeraro G (2017) Introducing linked open data in graph-based recommender systems. Inf Process Manag 53(2):405–435

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LISI Laboratory, Faculty of Sciences Semlalia Marrakech, Cadi Ayyad University, Marrakech, Morocco

    Sara Qassimi & El Hassan Abdelwahed

Authors
  1. Sara Qassimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. El Hassan Abdelwahed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sara Qassimi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qassimi, S., Abdelwahed, E.H. The role of collaborative tagging and ontologies in emerging semantic of web resources. Computing 101, 1489–1511 (2019). https://doi.org/10.1007/s00607-019-00704-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-019-00704-9

Keywords

Search

Navigation