Arnetminer: expertise oriented search using social
networks

doi:10.1007/s11704-008-0008-9

Front. Comput. Sci.

0, Vol.

Issue () : 94-105 https://doi.org/10.1007/s11704-008-0008-9

Arnetminer: expertise oriented search using social networks

LI Juanzi¹, TANG Jie¹, ZHANG Jing¹, HONG Mingcai¹, LUO Qiong², LIU Yunhao²

1.Department of Computer Science and Technology, Tsinghua University; 2.Department of Computer Science, The Hong Kong University of Science and Technology;

Download: PDF(404 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (http://www.arnetminer.net). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed two-stage association search on a large-scale social network is order of magnitude faster than the baseline method.

Issue Date: 05 March 2008

Cite this article:

LI Juanzi,TANG Jie,ZHANG Jing, et al. Arnetminer: expertise oriented search using social networks[J]. Front. Comput. Sci., 0, (): 94-105.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-008-0008-9
https://academic.hep.com.cn/fcs/EN/Y0/V/I/94

1	Citeseer. http://www.citeseer.nec.com
2	Aleman-Meza B Nagarajan M Ramakrishnan C et al.Semantic analytics on social networks: experiencesin addressing the problem of conflict of interest detectionIn: Proceedings of WWW' 06New YorkACM 2006 407416
3	Hettich S Pazzani M J Mining for proposal reviewers:lessons learned at the national science foundationIn: Proceedings of KDD' 06New YorkACM 2006 862871
4	Golbeck J Web-basedsocial networks: a survey and future directions. Technique Report
5	Brickley D Miller L FOAF vocabulary specificationNamespace Document, http://xmlns.com/foaf/0.1/
6	Kruk S R Decker S Semantic social collaborativefiltering with FOAFRealmIn: Proceedings of the SemanticDesktop Workshop Galway, Ireland 2005
7	Foafnaut. http://www.foafnaut.org/
8	Flink. http://flink.semanticweb.org/
9	Culotta A Bekkerman R McCallum A Extracting social networks and contact information fromemail and the webIn: Proceedings of Email and Spam 2004
10	Kautz H Selman B Shah M Combining social networks and collaborative filteringCommunications of the ACM 1997 New YorkACM40(3)6365
11	Mika P Flink:Semantic Web technology for the extraction and analysis of socialnetworksWeb Semantics: Science, Servicesand Agents on the World Wide Web 2005 3(2)211223
12	Matsuo Y Mori J Hamasaki M POLYPHONE: an advanced social network extraction systemfrom the webIn: Proceedings of WWW' 06New YorkACM 2006 397406
13	Balog K Rijke M d Searching for people in thepersonal work spaceIn: Proceedings of InternationalWorkshop on Intelligent Information Access 2006
14	Ding L Finin T Joshi A et al.Swoogle: a search and metadata engine for the semanticwebIn: Proceedings of the InternationalConference on Information and Knowledge MangementNew YorkACM 2004 652659
15	Libra. http://libra.msra.cn/
16	Rexa. http://rexa.info/
17	Doan A Ramakrishnan R Chen F et al.Community information managementData Enginneering 2006 29(1)6472
18	Hawking D Challengesin enterprise searchIn: Proceedings ofthe 15th Australasian database conferenceAustraliaAustralian Computer Society,Inc 2004 1524
19	Nascimento M A Sander J Pound J Analysis of SIGMOD's coauthorship graphACM SIGMOD Record 2003 32(3)810
20	Smeaton A F Keogh G Gurrin C et al.Analysis of papers from twenty-five years of SIGIRconferences: what have we been doing for the last quarter of a centuryACM SIGIR Forum 2002 36(2)3943
21	Campbell C S Maglio P P Cozzi A et al.Expertise identification using email communicationsIn: Proceedings of CIKM' 03New YorkACM 2003 528531
22	Schwartz M F Wood D C M Discovering shared interestsusing graph analysisCommunications of theACM 1993 36(8)7889
23	Foner L N Yenta:a multi-agent, referral-based matchmaking systemIn: Proceedings of First International Conference on Autonomous AgentsNew YorkACM 1997 301307
24	Mattox D Maybury M Morey D Enterprise expert and knowledge discoveryIn: Proceedings of HCIMahwahLawrence Erlbaum Associates, Inc 1999 303307
25	Seid D Y Kobsa A Expert finding systems fororganizations: problem and domain analysis and the demoir approachJournal of Organizational Computing and ElectronicCommerce 2003 13124
26	Craswell N Vries A P D Soboroff I Overview of the trec-2005 enterprise trackTREC 2005 Conference Notebook 2005 199205
27	Fu Y Yu W Li Y et al.THUIR at trec 2005: enterprise trackIn: Proceedings of TREC' 05 2005 733738
28	Cao Y Liu J Bao S et al.Research on expert search at enterprise track oftrec 2005In: Proceedings of TREC' 05. TREC,2005
29	Adamic L Adar E How to search a social networkSocial Networks 2005 27187203
30	Foaf. http://xmlns.com/foaf/0.1/
31	Carroll J J Reynolds D Dickinson I et al.Jena: implementing the semantic web recommendationsIn: Proceedings of WWW' 04New YorkACM 2004 7483
32	Liang B Tang J Li J SWARMS: a tool for exploring domain knowledgeIn: Proceedings of the Workshop of Contexts and Ontologieson the 20th International AAAI Conference 2005 120123
33	DBLP. http://www.informatik.uni-trier.de/∼ley/db/
34	Vapnik V Statisticallearning theoryNew YorkSpringer Verlage 1998
35	Tang J Li J Lu H et al.IASA: learning to annotate the semantic webJournal of Data Semantics 2005 4110145
36	Tang J Li H Cao Y et al.Email data cleaningIn: Proceedings of KDD' 05New YorkACM 2005 489498
37	Milgram S Thesmall world problemPsychology Today 1967 26067

Viewed

Full text

Abstract

Cited

Shared

Discussed