Article | 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15‚Äď16, 2017, Karlskrona, Sweden | Advanced Data-driven Techniques for Mining Expertise
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Title:
Advanced Data-driven Techniques for Mining Expertise
Author:
Milena Angelova: Technical University of Sofia-branch Plovdiv, Bulgaria Veselka Boeva: Blekinge Institute of Technology, Karlskrona, Sweden Elena Tsiporkova: Sirris, The Collective Center for the Belgian technological industry, Brussels, Belgium
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Full text (pdf)
Year:
2017
Conference:
30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15‚Äď16, 2017, Karlskrona, Sweden
Issue:
137
Article no.:
005
Pages:
45-52
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2017-05-12
ISBN:
978-91-7685-496-9
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In this work, we discuss enhanced techniques that optimize expert representation and identify subject experts via clustering analysis of the available online information. We use a weighting method to assess the levels of expertise of an expert to the domain-specific topics. In this context, we define a way to estimate the expertise similarity between experts. Then the experts finding task is viewed as a list completion task and techniques that return similar experts to ones provided by the user are considered. In addition, we discuss a formal concept analysis approach for clustering of a group of experts with respect to given subject areas. The produced grouping of experts can further be used to identify individuals with the required competence.

Keywords: Data mining, expert finding, health science, knowledge management

30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15‚Äď16, 2017, Karlskrona, Sweden

Author:
Milena Angelova, Veselka Boeva, Elena Tsiporkova
Title:
Advanced Data-driven Techniques for Mining Expertise
References:

[1] Autonomy Technology White Paper (http://www.autonomy.com) [2] Foner, L. “Yenta: a multi-agent referral system for matchmaking system”, Proceedings of the First International Conference on Autonomous Agents, Marina Del Ray, CA, 1997.


[3] Tacit Knowledge Systems’ KnowledgeMail (http://www.tacit.com)


[4] Kautz, H., Selman, B., Shah., M., “Referral Web: combining social networks and collaborative filtering” inCommunications of the ACM, Vol. 40, Issue 3, pp. 63-65, 1997.


[5] Ha-Thuc, V., Venkataraman, G., Rodriguez, M., Sinha, S., Sundaram, S., Guo, L. “Personalized Expertise Search at LinkedIn”, 2016.


[6] https://maj.io/#/


[7] http://yagajobs.co.uk


[8] Seid, D., Kobsa, A. “Demoir: A hybrid architecture for expertise modelling and recommender systems”. 2000.


[9] Campbell, C.S., “Expertise identification using Bibliography 189 email communications”, 12th Int. Conf. on Inform. and Knowl. Manag. ACM Press. 2003.


[10] D’Amore, R. “Expertise community detection”, 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM Press. 2004.


[11] Mockus, A., Herbsleb, J.D.“Expertise browser: a quantitative approach to identifying expertise”, 24th Int. Conf. on Software Engineering. ACM Press. 2002.


[12] Hawking, D.“Challenges in enterprise search”, 15th Australasian Database Conference. Australian Computer Society, Inc. 2004.


[13] Tsiporkova, E., Tourw√©, T.“Tool support for technology scouting using online sources” in Springerpp. 371‚Äď376. 2011.


[14] Singh, H.“Developing a Biomedical Expert Finding System Using Medical Subject Headings” inHealthcare Informatics ResearchVol. 19, Issue 4,pp. 243-249. 2013.


[15] Hristoskova, A.“A Graph-based Disambiguation Approach for Construction of an Expert Repository from Public Online Sources”, 5th IEEE Int. Conf. on Agents and Artificial Intelligence. 2013.


[16] Abramowicz, W.“Semantically Enabled Experts Finding System - Ontologies, Reasoning Approach and Web Interface Design” in ADBISVol. 2, pp. 157-166. 2011.


[17] Bozzon, A.“Choosing the Right Crowd: Expert Finding in Social Networks”, EDBT/ICDT’13. Genoa, Italy. 2013.


[18] Craswell, N. “Overview of the TREC-2005 Enterprise Track”, 14th Text Retrieval Conference. 2006.


[19] Balog, K.“People search in the enterprise”. PhD thesis, Amsterdam University. 2008.


[20] Toutanova, K. “Enriching the knowledge sources used in a maximum entropy part of speech tagger”, the Joint SIGDAT Conference on Empirical Methods in NLP and Very Large Corpora. EMNLP/VLC-2000. 2000.


[21] Buelens, S., Putman, M., “Identifying experts trough a framework for knowledge extraction from public online sources”. Master thesis, Gent University, Belgium, 2011.


[22] Boeva, V., Krusheva, M., Tsiporkova, E. “Measuring Expertise Similarity in Expert Networks”, Proceedings of the 6th IEEE Int. Conf. on Intelligent Systems, pp. 53-57, 2012.


[23] Boeva, V., et al. “Data-driven Techniques for Expert Finding”, ICAART 9th International Conference on Agents and Artificial Intelligence, pp. 535-542, Porto, 2017.


[24] Stankoff, D., Kruskal, J. “Time warps, string edits, and macromolecules: the theory and practice of sequence comparison”, Addison Wesley Reading Mass. 1983.


[25] Sakoe, H. and Chiba, S. “Dynamic programming algorithm optimization for spoken word recognition”. In IEEE Trans. On Acoust, Speech, and Signal Proc., ASSP-26, pp. 43-49, 1978.


[26] Cameron, D, L. “SEMEF: A Taxonomy-based Discovery of Experts, Expertise and Collaboration Networks”. MS thesis, The University of Georgia. 2007.


[27] Hecht, F. “The Journal Impact Factor: A Misnamed, Misleading, Misused Measure” in Cancer GenetCytogenet, Vol. 4, pp. 77-81, Elsevier Science Inc. 1998.


[28] Seglen, P. O. “Why the impact factor of journals should not be used for evaluating research” in BMJ. Vol. 314, Issue 7079, pp. 497. 1997.


[29] Hirch, J. E. “An index to quantify an individual’s scientific research output” inPNAS Vol. 102, Issue 46, pp. 16569-16572. 2005.


[30] Afzal, M.T., Maurer, H. “Expertise Recommender System for Scientific Community” in Journal of Universal Computer Science Vol. 17, Issue 11, pp. 1529-1549. 2011.


[31] Fellbaum, C., “WordNet: An Electronic Lexical Database”. MIT Press, Cambridge. 2001.


[32] Miller, G. A. “WordNet: A lexical Database for English” inCommunications of the ACM Vol. 38, Issue 11, pp. 39-41. 1995.


[33] Boeva, V. et al. “Measuring Expertise Similarity in Expert Networks”, In 6th IEEE Int. Conf. on Intelligent Systems, IS 2012 IEEE Sofia Bulgaria, pp. 53-57. 2012.


[34] Boeva, V. et al. “Semantic-aware Expert Partitioning” Artificial Intelligence: Methodology, Systems, and Applicationsin LNAI. Springer Int. Pub. Switzerland. 2014.


[35] B. Ganter, B., Stumme, G. and Wille, R. Formal Concept Analysis: Foundations and Applications, LNAI, no. 3626, Springer-Verlag, 2005.


[36] Zhou, J., Shui, Y. “The MeSHSim package”.


[37] Paliwal, K.K. et al. “A modification over Sakoe and Chiba’s Dynamic Time Warping Algorithm for Isolated Word Recognition” in Signal Proc Vol. 4, pp. 329-333. 1983.

30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15‚Äď16, 2017, Karlskrona, Sweden

Author:
Milena Angelova, Veselka Boeva, Elena Tsiporkova
Title:
Advanced Data-driven Techniques for Mining Expertise
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Last updated: 2017-02-21