Advertisement

Generation of User Interest Ontology Using ID3 Algorithm in the Social Web

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)

Abstract

It is feasible to collect individual user interests from social networking services. However, there have been few studies of the interests of domain users. In this paper, we propose an approach for ontology generating the interests of SNS domain users by employing semantic web technology and ID3 algorithm.In our approach, domain ontology is generated by a decision tree, which classifies the domain web pages and the domain users. Experimental test shows ontology of the interests of domains users regarding USA presidential candidates. We expect that our results will be beneficial in the field of computer science, such as recommendations, as well as other fields including education, politics, and commerce. Proposed approach overcomes the problem of domain user classification and lack of semantics by composing decision tree and semantic web technology.

Keywords

Semantic web Ontology ID3 algorithm SNS FOAF Interest extraction Social web Classification Election OWL 

References

  1. 1.
    Zhuge H (2010) Socio-natural thought semantic link network: a method of semantic networking in the cyber physical society perth. In: 24th IEEE international conference on advanced information networking and applications, pp 19–26Google Scholar
  2. 2.
    Zhang T, Lee B, Kang S, Kim H, Kim J (2009) Collective intelligence-based web page search: combining folksonomy and link-based ranking strategy. Computer and Information Technology, 2009, pp 116–171Google Scholar
  3. 3.
    Pi S, Liao H, Liu S, Lin C (2011) Framework for classifying website content based on folksonomy in social bookmarking. In: Intelligent computing and information science, communications in computer and information science, vol. 135. pp 250–255Google Scholar
  4. 4.
    Illig J, Hotho A, Jäschke R, Stumme G (2011) A comparison of content-based tag recommendations in folksonomy systems. In: Knowledge processing and data analysis, Lecture Notes in Computer Science, vol. 6581/2011, pp 136–149Google Scholar
  5. 5.
    ShanS, Zhang F, Wu X, Liu B, He Y (2011) Ranking tags and users for content-based item recommendation using folksonomy. Computing and Intelligent Systems, Communications in Computer and Information Science, pp 32–41Google Scholar
  6. 6.
    Szomszor M, Alani H, Cantador I, O’Hara K, Shadbolt N (2008) Semantic modelling of user interests based on cross-folksonomy analysis. In: The semantic web—ISWC, Lecture Notes in Computer Science, 2008, vol. 5318/2008. pp 632–648Google Scholar
  7. 7.
    Kawase R, Herder E (2011) Classification of user interest patterns using a virtual folksonomy JCDL’11, Ottawa, Canada, ACM 978-1-4503-0744-4/11/06, 13–17 June 2011Google Scholar
  8. 8.
    Lipczak M (2008) Tag recommendation for folksonomies oriented towards individual users. In: ECML PKDD Discovery Challenge, pp 84–95Google Scholar
  9. 9.
    Yin D, Hong L, Xue Z, Davison, BD (2011) Temporal dynamics of user interests in tagging systems. In: Twenty-Fifth AAAI conference on artificial intelligenceGoogle Scholar
  10. 10.
    Sasaki K, Okamoto M, Watanabe N, Kikuchi M, Iida T, Hattori M (2011) Extracting preference terms from web browsing histories excluding pages unrelated to users’ interests. In: SAC’11, TaiChung, Taiwan, pp 21–25 March 2011Google Scholar
  11. 11.
    White RW, Bailey P, Chen L (2009) Predicting user interests from contextual information. In: 32nd international ACM SIGIR conference on research and development in information retrieval, ACM New York, USA, pp 19–23Google Scholar
  12. 12.
    Argentiero P (1982) An automated approach to the design of decision tree classifiers. In: IEEE transactions on pattern analysis and machine intelligence, vol. Pami-4, no. 1Google Scholar
  13. 13.
    LópezMántaras R (1991) A distance-based attribute selection measure for decision tree induction. Mach Learn 6(1):81–92Google Scholar
  14. 14.
    Panigrahi S, Biswas S (2011) Next generation semantic web and its application. IJCSI Int J Comput Sci Issues 8(2):385–392Google Scholar
  15. 15.
    Gruber T (2008) What is an ontology. Encyclopedia of database systems, vol. 1. Springer-VerlagGoogle Scholar
  16. 16.
    vanRijsbergen CJ (1979) Information retrieval, Butterworth-Heinemann Newton, MAGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceKorea UniversitySejong CitySouth Korea

Personalised recommendations