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Technologies | Classic
Classic technology
CORDER
Semantic Web and Knowledge Services
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.
Participant: Jianhan Zhu, Enrico Motta, Victoria Uren, Alexandre L. Goncalves, Roberto Pacheco
Technology Champion: Enrico Motta
URL: http://kmi.open.ac.uk/projects/corder/
External Publications
Zhu, J., Eisenstadt, M., Gonçalves, A., Denham, C., Uren, V. and Song, D. (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
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
Zhu, J., Gonçalves, A., Uren, V., Motta, E., Pacheco, R., Eisenstadt, M. and Song, D. (2007) Relation Discovery from Web Data for Competency Management, Web Intelligence and Agent Systems: An International Journal, 5, 4, IOS Press
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
