@inproceedings{Liu_2002_Expertise-Matching,
	title = {Exploring RDF for Expertise Matching within an Organizational Memory.},
	author = {Ping Liu and Jayne Curson and Peter M. Dew},
	booktitle = {International Conference of Advanced Information Systems Engineering (CAiSE)},
	crossref = {conf/caise/2002},
	editor = {Anne Banks Pidduck and John Mylopoulos and Carson C. Woo and M. Tamer Özsu},
	pages = {100-116},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	url = {http://dblp.uni-trier.de/db/conf/caise/caise2002.html#LiuCD02},
	volume = {2348},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2ce1c33586052052e5c63acac6fcd8580/tobold},
	description = {dblp},
	abstract = {Organizations have realized that effective development and
management of their organizational knowledge base is very important
for their survival in todays competitive business environment. People,
as a special knowledge asset, also attract the interest of many
researchers because, only through people communicating with one
another, can they really share their tacit knowledge and skills that can
be more valuable than explicit documentation. The need to be able to
quickly locate experts among the heterogeneous data sources stored in
the organizational memory has been recognized by many researchers.
This paper examines the advantages of using RDF (Resource
Description Framework) for Expertise Matching. The major challenge
is to semantically integrate heterogeneous data sources stored in the
organizational memory and facilitate users to locate the right people.
We present a practical application of this using a case study where PhD
applicants can locate potential supervisors before they formally apply to
a university.
},
	date = {2002-06-05}, ee = {http://link.springer.de/link/service/series/0558/bibs/2348/23480100.htm}, isbn = {3-540-43738-X},
	keywords = {competency expert_finding }
}