@inproceedings{Jung/2007/Finding,
	title = {Finding Topic-centric Identified Experts based on Full Text Analysis},
	author = {Hanmin Jung},
	booktitle = {Proceedings of the Workshop on Finding Experts on the Web with Semantics (FEWS2007) at ISWC/ASWC2007, Busan, South Korea},
	crossref = {http://data.semanticweb.org/workshop/fews/2007/proceedings},
	editor = {Anna V. Zhdanova and Lyndon J B Nixon and Malgorzata Mochol and John Breslin},
	month = {November},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/213f7934be0d6c1174cff79fc9f53dbb6/ontoman},
	abstract = {This paper shows a method for finding topic-centric experts from open access metadata and full text documents. Topic-centric information including experts is served on OntoFrame, which is a Semantic Web-based academic research information service supporting R&D activities. URI scheme-based OntoFrame provides three entity pages: topic, person, and event. ‘Persons by Topic’ in topic page lists up topic-centric identified experts. SPARQL query is used to re-trieve them from RDF triple store through backward chaining. We gathered CiteSeer open access metadata and full text documents with the amount of about 110,000 papers. Using about 160,000 abundant topics, OntoFrame now serves topic-centric identified experts and relevant information acquired by full text analysis.},
	keywords = {Analysis analysis based expert experts finding text topic workshop_fews }
}