Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bloggers as experts: feed distillation using expert retrieval models
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Tag and Tagging to Learn: A Case Study on Wikipedia
IEEE Intelligent Systems
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
CAFE: Collaboration Aimed at Finding Experts
International Journal of Knowledge and Web Intelligence
Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Using Web-Mining for Academic Measurement and Scholar Recommendation in Expert Finding System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
ELIxIR: Expertise Learning and Identification x Information Retrieval
International Journal of Information Systems and Social Change
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In this paper, expert-finding problem is transformed to a classification issue. We build a knowledge database to represent the expertise characteristic of domain from web information constructed by collaborative intelligence, and an incremental learning method is proposed to update the database. Furthermore, results are ranked by measuring the correlation in the concept network from online encyclopedia. In our experiments, we use the real world dataset which comprise 2,701 experts who are categorized into 8 expertise domains. Our experimental results show that the expertise knowledge extracted from collaborative intelligence can improve efficiency and effect of classification and increase the precision of ranking expert at least 20%.