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Expertise Mining for Enterprise Content Management

Georgeta Bordea, Sabrina Kirrane, Paul Buitelaar, Bianca Pereira


Abstract
Enterprise content analysis and platform configuration for enterprise content management is often carried out by external consultants that are not necessarily domain experts. In this paper, we propose a set of methods for automatic content analysis that allow users to gain a high level view of the enterprise content. Here, a main concern is the automatic identification of key stakeholders that should ideally be involved in analysis interviews. The proposed approach employs recent advances in term extraction, semantic term grounding, expert profiling and expert finding in an enterprise content management setting. Extracted terms are evaluated using human judges, while term grounding is evaluated using a manually created gold standard for the DBpedia datasource.
Anthology ID:
L12-1190
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3495–3498
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/379_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Georgeta Bordea, Sabrina Kirrane, Paul Buitelaar, and Bianca Pereira. 2012. Expertise Mining for Enterprise Content Management. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3495–3498, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Expertise Mining for Enterprise Content Management (Bordea et al., LREC 2012)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/379_Paper.pdf