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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

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CORDER

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

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

Publications | Visit External Site for Details  

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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Knowledge Media Institute (KMi)
29,799 - 38,833 (Grade AC1 / AC2)
Based in Milton Keynes
Temporary contract until 31st December 2018

WE ACCEPT APPLICATIONS FROM CITIZENS GLOBALLY The Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of Science, Technology, Engineering and Mathematics (STEM) at the...

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