Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 2/2017

04.02.2017

A survey on expert finding techniques

verfasst von: Shuyi Lin, Wenxing Hong, Dingding Wang, Tao Li

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.
Vorheriger Artikel Location detection and disambiguation from twitter messages
Nächster Artikel Towards portable natural language interfaces based on case-based reasoning

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
Amati, G., & Van Rijsbergen, C.J. (2002). Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS), 20(4), 357–389.
Anvik, J., & Murphy, G.C. (2007). Determining implementation expertise from bug reports. In Fourth International Workshop on Mining Software Repositories (MSR’07: ICSE Workshops 2007) (pp. 2–2). IEEE.
Bailey, P., De Vries, A.P., Craswell, N., & Soboroff, I. (2007). Overview of the TREC 2007 enterprise track. In TREC.
Balog, K. (2007). People search in the enterprise. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 916–916). ACM.
Balog, K. (2008). The SIGIR 2008 workshop on future challenges in expertise retrieval (fCHER). In ACM SIGIR Forum (Vol. 42, No. 2, pp. 46–52). ACM.
Balog, K., Azzopardi, L., & De Rijke, M. (2006). Formal models for expert finding in enterprise corpora. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 43–50). ACM.
Balog, K., Azzopardi, L., & De Rijke, M. (2009). A language modeling framework for expert finding. Information Processing & Management, 45(1), 1–19 0306–4573.CrossRef
Balog, K., Bogers, T., Azzopardi, L., De Rijke, M., & Van Den Bosch, A. (2007). Broad expertise retrieval in sparse data environments. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 551–558). ACM.
Balog, K., & De Rijke, M. (2007). Determining expert profiles (with an application to expert finding). In IJCAI (Vol. 7, pp. 2657–2662).
Balog, K., De Rijke, M., & Weerkamp, W. (2008). Bloggers as experts: feed distillation using expert retrieval models. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 753–754). ACM.
Balog, K., Hofmann, K., Weerkamp, W., & De Rijke M. (2007). Query and document models for enterprise search.
Balog, K., Yi, F., De Rijke, M., Serdyukov, P., & Si, L. (2012). Expertise retrieval. Foundations and Trends in Information Retrieval, 6(2-3), 127–256.CrossRef
Basu, S., Bilenko, M., & Mooney, R.J. (2004). A probabilistic framework for semi-supervised clustering. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 59–68). ACM.
Becerra-Fernandez, I (2001). Locating expertise at nasa: developing a tool to leverage human capital. Knowledge Management Review, 4(4), 34–37.
Bekkerman, R., & McCallum, A. (2005). Disambiguating web appearances of people in a social network. In Proceedings of the 14th international conference on World Wide Web (pp. 463-470). ACM.
Berendsen, R., Rijke, M., Balog, K., Bogers, T., & Bosch, A. (2013). On the assessment of expertise profiles. Journal of the American Society for Information Science and Technology, 64(10), 2024–2044.CrossRef
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american, 284(5), 28– 37.CrossRef
Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. the Journal of machine Learning 744 research, 3, 993–1022 1532–4435.MATH
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks 776 and ISDN systems, 30(1), 107–117 0169–7552.CrossRef
Campbell, C.S., Maglio, P.P., Cozzi, A., & Dom, B. (2003). Expertise identification using email communications. In Proceedings of the twelfth international conference on Information and knowledge management (pp. 528–531). ACM.
Cao, Y., Liu, J., Bao, S., & Li, H. (2005). Research on Expert Search at Enterprise Track of TREC 2005.
Chen, H., Shen, H., Xiong, J., Tan, S., & Cheng, X. (2006). Social network structure behind the mailing lists: ICT-IIIS at TREC 2006 expert finding track. In TREC.
Conrad, J.G., & Utt, M.H. (1994). A system for discovering relationships by feature extraction from text databases: Springer.
Craswell, N, de Vries, A.P., & Soboroff, I. (2005). Overview of the TREC enterprise track. In Trec (Vol. 5, pp. 199–205).
Craswell, N., Hawking, D., Vercoustre, A.M., & Wilkins, P. (2001). P@noptic expert: Searching for experts not just for documents. In Ausweb Poster Proceedings, Queensland, Australia (Vol. 15, p. 17).
Culotta, A., Bekkerman, R., & McCallum, A. (2004). Extracting social networks and contact information from email and the web.
D’Amore, R. (2004). Expertise community detection. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 498–499). ACM.
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., & Harshman, R.A. (1990). Indexing by latent semantic analysis. JASIS, 41(6), 391–407.CrossRef
Deng, H., King, I., & Lyu, M.R. (2008). Formal models for expert finding on DBLP bibliography data. In 2008 Eighth IEEE International Conference on Data Mining (pp. 163–172). IEEE.
Dom, B., Eiron, I., Cozzi, A., & Zhang, Y. (2003). Graph-based ranking algorithms for e-mail expertise analysis. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery (pp. 42–48). ACM.
Duan, H., Zhou, Q., Lu, Z., Jin, O., Bao, S., Cao, Y., & et al. (2007). Research on enterprise track of TREC 2007 at SJTU APEX lab. (Vol.22).
Dumais, S.T., & Nielsen, J. (1992). Automating the assignment of submitted manuscripts to reviewers. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 233–244). ACM.
Ehrlich, K., Lin, C.Y., & Griffiths-Fisher, V. (2007). Searching for experts in the enterprise: combining text and social network analysis. In Proceedings of the 2007 international ACM conference on Supporting group work (pp. 117–126). ACM.
Fang, H., & Zhai, C.X. (2007). Probabilistic models for expert finding, Springer.
Fang, Y., Si, L., & Mathur, A.P. (2011). Discriminative probabilistic models for expert search in heterogeneous information sources. Information Retrieval, 14(2), 158–177.CrossRef
Foner, L.N. (1997). Yenta: a multi-agent, referral-based matchmaking system. In Proceedings of the first international conference on Autonomous agents (pp. 301–307). ACM.
Fox, E.A., & Shaw, J.A. (1994). Combination of multiple searches. NIST SPECIAL PUBLICATION SP, 243–243.
Fu, Y., Xiang, R., Liu, Y., Zhang, M., & Ma, S. (2007). Finding experts using social network analysis. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (pp. 77–80). IEEE Computer Society.
Greer, J.E., Mccalla, G., Collins, J.A., Kumar, V.S., Meagher, P., & Vassileva, J. (1998). Supporting peer help and collaboration in distributed workplace environments. International Journal of Artificial Intelligence in Education (IJAIED), 9, 159–177.
Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics[J]. Proceedings of the National academy of Sciences, 101(Suppl 1), 5228–5235.CrossRef
Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two supervised learning approaches for name disambiguation in author citations. In Digital Libraries, 2004. Proceedings of the 2004 joint ACM/IEEE conference on (pp. 296–305). IEEE.
Han, H., Zha, H., & Giles, C.L. (2005). Name disambiguation in author citations using a k-way spectral clustering method. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’05) (pp. 334–343). IEEE.
Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 50–57). ACM.
Hu, G., Liu, J., Li, H., Cao, Y., Nie, J.Y., & Gao, J. (2006). A supervised learning approach to entity search. In Asia Information Retrieval Symposium (pp. 54–66). Springer Berlin Heidelberg.
Kautz, H., Selman, B., & Shah, M. (1997). The hidden web. AI magazine, 18(2), 27.
Lavrenko, V., & Croft, W.B. (2001). Relevance based language models. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 120–127). ACM.
Lee, J.H. (1997). Analyses of multiple evidence combination. In ACM SIGIR Forum (Vol. 31, No. SI, pp. 267–276). ACM.
Macdonald, C. (2009). The voting model for people search (Doctoral dissertation, University of Glasgow).
Macdonald, C., & Ounis, I. (2006). Voting for candidates: adapting data fusion techniques for an expert search task. In Proceedings of the 15th ACM international conference on Information and knowledge management (pp. 387–396). ACM.
Macdonald, C., & Ounis, I. (2007). Using relevance feedback in expert search. In European Conference on Information Retrieval (pp. 431–443). Springer Berlin Heidelberg.
McCallum, A., Wang, X., & Corrada-Emmanuel, A. (2007). Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial Intelligence Research (JAIR), 30, 249–272.
McDonald, D.W., & Ackerman, M.S. (2000). Expertise recommender: a flexible recommendation system and architecture. In Proceedings of the 2000 ACM conference on Computer supported cooperative work(pp. 231–240). ACM.
McLean, A., Wu, M., & Vercoustre, A.M. (2005). Combining structured corporate data and document content to improve expertise finding. arXiv:cs/​0509005.
Mattox, D., Maybury, M.T., & Morey, D. (1999). Enterprise expert and knowledge discovery. In HCI (2)(pp. 303–307).
Maybury, M., D’Amore, R., & House, D. (2001). Expert finding for collaborative virtual environments. Communications of the ACM, 44(12), 55–56.CrossRef
Mimno, D., & McCallum, A. (2007). Expertise modeling for matching papers with reviewers. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 500–509). ACM.
Mockus, A., & Herbsleb, J.D. (2002). Expertise browser: a quantitative approach to identifying expertise. In Proceedings of the 24th international conference on software engineering (pp. 503–512). ACM.
Montague, M., & Aslam, J.A. (2001). Relevance score normalization for metasearch. In Proceedings of the tenth international conference on Information and knowledge management (pp. 427–433). ACM.
Petkova, D., & Croft, W.B. (2007). Proximity-based document representation for named entity retrieval. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (pp. 731–740). ACM.
Petkova, D., & Croft, W.B. (2008). Hierarchical language models for expert finding in enterprise corpora. International Journal on Artificial Intelligence Tools, 17(01), 5–18.CrossRef
Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In Proceedings of the 20th conference on Uncertainty in artificial intelligence (pp. 487–494). AUAI Press.
Sanderson, M. (2010). Test collection based evaluation of information retrieval systems: Now Publishers Inc.
Schuler, D., & Zimmermann, T. (2008). Mining usage expertise from version archives. In Proceedings of the 2008 international working conference on Mining software repositories (pp. 121–124). ACM.
Serdyukov, P., Chernov, S., & Nejdl, W. (2007). Enhancing expert search through query modeling. In European Conference on Information Retrieval (pp. 737–740). Springer Berlin Heidelberg.
Serdyukov, P., Rode, H., & Hiemstra, D. (2008). Modeling multi-step relevance propagation for expert finding. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 1133–1142). ACM.
Shakery, A., & Zhai, C.X. (2006). A probabilistic relevance propagation model for hypertext retrieval. In Proceedings of the 15th ACM international conference on Information and knowledge management (pp. 550–558). ACM.
Stankovic, M., Wagner, C., Jovanovic, J., & Laublet, P. (2010). Looking for experts? what can linked data do for you?. In LDOW.
Tang, J., Fong, A.C.M., Wang, B., & Zhang, J. (2012). A unified probabilistic framework for name disambiguation in digital library. IEEE Transactions on Knowledge and Data Engineering, 24(6), 975–987.CrossRef
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 990–998). ACM.
Yao, C., Bo, P., He, J., & Yang, Z. (2005). CNDS. Expert Finding System for TREC 2005. Fourteenth Text Retrieval Conference, Trec 2005, Gaithersburg, Maryland, November. DBLP.
Yimam-Seid, D., & Kobsa, A. (2003). Expert-finding systems for organizations: Problem and domain analysis and the demoir approach. Journal of Organizational Computing and Electronic Commerce, 13(1), 1–24.CrossRef
Zhai, C., & Lafferty, J. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 334–342). ACM.
Zhang, J., Tang, J., & Li, J. (2007). Expert finding in a social network. In International Conference on Database Systems for Advanced Applications (pp. 1066–1069). Springer Berlin Heidelberg.
Zhang, J., Ackerman, M.S., & Adamic, L. (2007). Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th international conference on World Wide Web (pp. 221–230). ACM.
Zhang, M., Song, R., Lin, C., Ma, S., Jiang, Z., Jin, Y., Liu, Y., Zhao, L., & Ma, S. (2003). Expansion-based technologies in finding relevant and new information: thu trec 2002: novelty track experiments. NIST SPECIAL PUBLICATION SP, 251, 586–590.
Zhu, J., Song, D., & Rüger, S. (2009). Integrating multiple windows and document features for expert finding. Journal of the American Society for Information Science and Technology, 60(4), 694–715.CrossRef
Metadaten
Titel
A survey on expert finding techniques
verfasst von
Shuyi Lin
Wenxing Hong
Dingding Wang
Tao Li
Publikationsdatum
04.02.2017
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 2/2017
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-016-0440-5

Weitere Artikel der Ausgabe 2/2017

Journal of Intelligent Information Systems 2/2017 Zur Ausgabe

OriginalPaper

An audio-visual corpus for multimodal automatic speech recognition

OriginalPaper

Distrust seed set propagation algorithm to detect web spam

OriginalPaper

Towards portable natural language interfaces based on case-based reasoning

OriginalPaper

Temporal Query Processing in Social Network

OriginalPaper

Location detection and disambiguation from twitter messages

OriginalPaper

A video summarization approach based on the emulation of bottom-up mechanisms of visual attention