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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.


Zhu, Jian-Han, Goncalves, Alexandre L., Uren, Victoria, Motta, Enrico, Pacheco, Roberto, Song, Dawei, R├╝ger, Stefan, Community Relation Discovery by Named Entities

Zhu, J.L., Goncalves, A, Uren, V., Motta, E., Pacheco, R, Eisenstadt, M., Song, D., Relation Discovery from Web Data for Competency Management

Dzbor, M., Stutt, A., Motta, E., Collins, T., Representations for semantic learning webs: Semantic Web technology in learning support


Jianhan Zhu Victoria Uren Alexandre L. Goncalves Roberto Pacheco Enrico Motta


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