Name
Abstract | ||
|---|---|---|
In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder. |
Year | Venue | Keywords |
|---|---|---|
2007 | Web Intelligence and Agent Systems | expert evaluation,relation discovery,web data,entity recognition,initial evaluation experiment,knowledge management,unsupervised learner,named entity recognition,novel unsupervised machine,competency management,co-occurrence data,unstructured data,structured information,clustering,unstructured information,unsupervised machine learning,social network,web pages |
Field | DocType | Volume |
Data science,Data mining,Competence (human resources),Social network,Web page,Computer science,Unstructured data,Unsupervised learning,Artificial intelligence,Benchmarking,Entity linking,World Wide Web,Named-entity recognition,Machine learning | Journal | 5 |
Issue | Citations | PageRank |
4 | 5 | 1.41 |
References | Authors | |
20 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhan Zhu | 1 | 474 | 28.87 |
| Alexandre L. Gonçalves | 2 | 17 | 6.22 |
| Victoria Uren | 3 | 1184 | 78.67 |
| Enrico Motta | 4 | 4216 | 391.29 |
| Roberto Pacheco | 5 | 38 | 6.42 |
| Marc Eisenstadt | 6 | 342 | 71.18 |
| Dawei Song | 7 | 472 | 45.59 |