An Ontology Based Model for Experts Search and Ranking

Experts finding is an important issue for finding potential contributors or expertise in a specific field. In scientific research, researchers often try to find an experts list related to their interest areas to acquire the knowledge about state arts of current research and novices can get benefit to find new ideas for research. In this paper, we proposed an ontological model to find and rank the experts in a particular domain. First, an Academic Knowledge Base(AKB) is built for a particular domain and then an academic social network (ASN) is constructed based on the information provided by the knowledge base for a given topic. In our approach, we proposed a cohesive modeling approach to investigate academic information considering heterogeneous relationship. Our proposed model provides a novel approach to organize and manage the real world academic information in a structural way which can share and reuse by others. Based on this structured academic information an academic social network is built to find the experts for a particular topic. Moreover, the academic social network ranks the experts with a ranking scores depending upon relationships among expert candidates. Finally, we verify the experimental evaluations of our model which improve precision of finding experts compare to baseline methods.

[1]  Ngoc Thanh Nguyen,et al.  Constructing and mining a semantic-based academic social network , 2010, J. Intell. Fuzzy Syst..

[2]  Haiqiang Chen,et al.  Social Network Structure Behind the Mailing Lists: ICT-IIIS at TREC 2006 Expert Finding Track , 2006, TREC.

[3]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[4]  Steffen Staab,et al.  The Semantic Web - ISWC 2008, 7th International Semantic Web Conference, ISWC 2008, Karlsruhe, Germany, October 26-30, 2008. Proceedings , 2008, SEMWEB.

[5]  Yong Yu,et al.  A Probabilistic Model for Fine-Grained Expert Search , 2008, ACL.

[6]  M. de Rijke,et al.  Formal models for expert finding in enterprise corpora , 2006, SIGIR.

[7]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[8]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[9]  Nicola Guarino,et al.  OntoSeek: content-based access to the Web , 1999, IEEE Intell. Syst..

[10]  Antal van den Bosch,et al.  Using Citation Analysis for Finding Experts in Workgroups , 2008 .

[11]  Ellen M. Voorhees,et al.  Retrieval evaluation with incomplete information , 2004, SIGIR '04.

[12]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.

[13]  Teresa M. Harrison,et al.  The Electronic Journal as the Heart of an Online Scholarly Community , 1995 .

[14]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Dan Brickley,et al.  FOAF Vocabulary Specification , 2004 .

[16]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[17]  Gang Wu,et al.  Identifying Potentially Important Concepts and Relations in an Ontology , 2008, International Semantic Web Conference.

[18]  GeunSik Jo,et al.  A Collaborative Ontology-Based User Profiles System , 2009, ICCCI.

[19]  Hongbo Deng,et al.  Formal Models for Expert Finding on DBLP Bibliography Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[20]  GeunSik Jo,et al.  Contextual Information Search Based on Ontological User Profile , 2010, ICCCI.