OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying Potentially Important Concepts and Relations in an Ontology
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Collaborative Ontology-Based User Profiles System
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Constructing and mining a semantic-based academic social network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Knowledge integration and management in autonomous systems
Contextual information search based on ontological user profile
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
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.