The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July/August (2009 vol.13)
pp: 59-67
Marko Grobelnik , Jozef Stefan Institute
Dunja Mladenic , Jozef Stefan Institute
Blaž Fortuna , Jozef Stefan Institute
ABSTRACT
Using semantic technologies in various domains, researchers have developed domain-specific ontologies to capture knowledge and enable reasoning. Organizations can support such knowledge management by capturing knowledge of their own people and their communication records, including email exchanges. The proposed approach analyzes an internal social network and uses the resulting information to produce an informal organizational structure. The authors evaluated their approach using a mid-size organization's actual data and compared the informal structure they obtained with the formal organizational structure. As the results show, the approach proved useful for modeling social structures based on real-world communication records.
INDEX TERMS
knowledge management, organizational structure, knowledge discovery, semi-automatic ontology learning, email transactions
CITATION
Marko Grobelnik, Dunja Mladenic, Blaž Fortuna, "Semantic Technology for Capturing Communication Inside an Organization", IEEE Internet Computing, vol.13, no. 4, pp. 59-67, July/August 2009, doi:10.1109/MIC.2009.88
REFERENCES
1. M. Grobelnik and D. Mladenić, "Automated Knowledge Discovery in Advanced Knowledge Management," J. Knowledge Management, vol. 9, no. 5, 2005, pp. 32–149.
2. S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences, Cambridge Univ. Press, 1994.
3. M. Grobelnik, D. Mladenić, and B. Fortuna, "Ontology Generation from Social Networks," Semantic Knowledge Management: Integrating Ontology Management, Knowledge Discovery, and Human Language Technology, Davies et al., eds., Springer, 2009, pp. 129–139.
4. J.R. Tyler, D.M. Wilkinson, and B.A. Huberman, "E-mail As Spectroscopy: Automated Discovery of Community Structure within Organizations," Communities and Technologies, Kluwer, 2003, pp. 81–96.
5. D. Mladenić and M. Grobelnik, "Visualizing Very Large Graphs Using Clustering Neighborhoods," Local Pattern Detection, LNCS 3539, Springer, 2005, pp. 89–97.
6. C. Olston and H.E. Chi, "Scenttrails: Integrating Browsing and Searching on the Web," ACM Trans. Computer-Human Interaction, vol. 10, no. 3,ACM Press, 2003, pp. 177–197.
7. C.J. Van Rijsbergen, Information Retrieval, Butterworths, 1979.
8. W.W. Cohen, P. Ravikumar, and S.E. Fienberg, "A Comparison of String Distance Metrics for Name-Matching Tasks," Proc. IJCAI03 Workshop on Information Integration on the Web, ACM Press, 2003, pp. 73–78.
9. B. Fortuna, M. Grobelnik, and D. Mladenić, "Semi-Automatic Construction of Topic Ontology," Proc. 2nd Int'l Workshop on Knowledge Discovery and Ontologies (KDO 05), 2005; https://webhosting.vse.cz/svatek/KDO05paper4.pdf .
10. B. Fortuna, D. Mladenić, and M. Grobelnik, "Visualization of Text Document Corpus," Informatica J. (Ljubljana), vol. 29, no. 4, 2005, pp. 497–502.
476 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool