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Champion: Enrico Motta
Professor of Knowledge Technologies Email Icon Website Icon RDF Icon

Participant(s):Jianhan Zhu, Victoria Uren, Alexandre L. Goncalves, Roberto Pacheco

Timeline:22 Dec 2005



COmunity Relation Discovery by named Entity Recognition

CORDER (COmmunity Relation Discovery by named Entity Recognition) is an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in a community with their expertise and associates. CORDER discovers relations from the Web pages of the community. Its approach is based on co-occurrences of NEs and the distances between them. For a given NE, there are a number of co-occurring NEs. We assume that NEs that are closely related to each other tend to appear together more often and closer to each other in Web pages. We calculate a relation strength for each co-occurring NE based on its co-occurrences and distances from the given NE. The co-occurring NEs are ranked by their relation strengths.


Publications | Download PDF  

Zhu, J., (2007) Social Search With Missing Data: Which Ranking Algorithm?, Journal of Digital Information Management special issue on Web Information Retrieval, eds. Pit.Pichappan, Keith van Rijsbergen, and Iadh Ounis, Digital Information Research Foundation

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Zhu, J., Goncalves, A., Uren, V., Motta, E., Pacheco, R., Song, D. and Rueger, S. (2007) Community Relation Discovery by Named Entities, International Conference on Machine Learning and Cybernetics 2007, Hong Kong, China, IEEE

Publications | Download PDF Publications | Visit External Site for Details  

Zhu, J., (2007) Relation Discovery from Web Data for Competency Management, Web Intelligence and Agent Systems: An International Journal, 5, 4, IOS Press

Publications | Download PDF Publications | Visit External Site for Details Publications | doi

Dzbor, M., Stutt, A., Motta, E. and Collins, T. (2007) Representations for semantic learning webs: Semantic Web technology in learning support, Journal of Computer Assisted Learning, 23, 1, pp. 69-82, Blackwell Publishing Ltd., UK

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