An Ontology-Based Methodology for Building and Matching Researchers’ Profiles

  • Nawarat Kamsiang
  • Twittie Senivongse
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


To support potential research collaboration, we present an ontology-based methodology for identifying common research interest among researchers. The methodology uses an ontology building algorithm to build researchers’ ontological profiles from publication keywords, and then an ontology matching algorithm is used to identify common research areas and degree of matching between research profiles. Our ontology matching also considers depth weights, i.e., the depth of the ontological terms within the two profiles that are matched. The idea is the terms that are located near the bottom of the ontologies should indicate specialization of researchers, and hence attention should be paid more to matching of such terms than to matching of the terms that are closer to the top of the ontologies. We present an experiment to match profiles of researchers in the same field, close fields, and different fields, and report the performance of the methodology and an evaluation using an ontology matching benchmark. The methodology is considered useful as it can quantify similarity of research interests and give practical matching results.


Neighbor search algorithm Ontology building Ontology matching Profile matching Research expertise WordNet 


  1. 1.
    Okubo Y (1997) Bibliometric indicators and analysis of research systems: methods and examples. OECD Publishing, ParisCrossRefGoogle Scholar
  2. 2.
    Kamsiang N, Senivongse T (2012) Identifying common research interest through matching of ontological research profiles, lecture notes in engineering and computer science. In: Proceedings of the world congress on engineering and computer science 2012, WCECS 2012, 24–26 Oct. USA, San Francisco, pp 380–385Google Scholar
  3. 3.
    Kamsiang N, Senivongse T (2012) An ontological analysis of common research interest for researchers. In: Proceedings of 8th international conference on computing and information technology (IC2IT 2012), pp 163–168Google Scholar
  4. 4.
    Ontology alignment evaluation initiative 2012 campaign. Available:
  5. 5.
    Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) ArnetMiner: extraction and mining of academic social networks. In: Proceedings of 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2008), pp 990–998Google Scholar
  6. 6.
    Zhang J, Ackerman M, Adamic L (2007) Expertise network in online communities: structure and algorithms. In: Proceedings of 16th international world wide web conference (WWW 2007), pp 221–230Google Scholar
  7. 7.
    Punnarut R, Sriharee G (2010) A researcher expertise search system using ontology-based data mining. In: Proceedings of 7th Asia-Pacific conference on conceptual modelling (APCCM 2010), pp 71–78Google Scholar
  8. 8.
    Trigo L (2011) Studying researcher communities using text mining on online bibliographic databases. In: Proceedings of 15th Portuguese conference on artificial intelligence, pp 845–857Google Scholar
  9. 9.
    Yang Y, Yueng CA, Weal MJ, Davis HC (2009) The researcher social network: a social network based on metadata of scientific publications. In: Proceedings of web science conference 2009 (WebSci 2009)Google Scholar
  10. 10.
    ISI web of knowledge. Available:
  11. 11.
    An YJ, Geller J, Wu Y, Chun SA (2007) Automatic generation of ontology from the deep web. In: Proceedings of 18th international workshop on database and expert systems applications (DEXA’07), pp 470–474Google Scholar
  12. 12.
    WordNet. Available:
  13. 13.
    Alasoud A, Haarslev V, Shiri N (2008) An effective ontology matching technique. In: Proceedings of 17th international conference on foundations of intelligent systems, pp 585–590Google Scholar
  14. 14.
    Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33:31–88CrossRefGoogle Scholar
  15. 15.
    Wordnet::Similarity. Available:
  16. 16.
    Yang H, Liu S, Fu P, Qin H, Gu L (2009) A semantic distance measure for matching web services. In: Proceedings of international conference on computational intelligence and software engineering (CiSE), pp 1–3Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Office of Computer ServiceSripatum University Chonburi CampusChonburiThailand
  2. 2.Department of Computer EngineeringChulalongkorn UniversityBangkokThailand

Personalised recommendations