EFS: Expert Finding System based on Wikipedia link pattern analysis
Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts'...
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| creator | Kai-Hsiang Yang Chun-Yu Chen Hahn-Ming Lee Jan-Ming Ho |
| description | Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia Web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like ldquoArtificial Intelligencerdquo and ldquoImage & Pattern Recognitionrdquo etc. |
| doi_str_mv | 10.1109/ICSMC.2008.4811348 |
| format | Conference Proceeding |
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Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia Web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like ldquoArtificial Intelligencerdquo and ldquoImage & Pattern Recognitionrdquo etc.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 142442383X</identifier><identifier>ISBN: 9781424423835</identifier><identifier>EISSN: 2577-1655</identifier><identifier>EISBN: 1424423848</identifier><identifier>EISBN: 9781424423842</identifier><identifier>DOI: 10.1109/ICSMC.2008.4811348</identifier><identifier>LCCN: 2008903109</identifier><language>eng</language><publisher>IEEE</publisher><subject>Application software ; Automatic Term Recognition ; Computer science ; Electronic mail ; Expert Finding ; Information science ; Ontologies ; Pattern analysis ; Proposals ; Social network services ; Wikipedia</subject><ispartof>2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, p.631-635</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4811348$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27916,54911</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4811348$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kai-Hsiang Yang</creatorcontrib><creatorcontrib>Chun-Yu Chen</creatorcontrib><creatorcontrib>Hahn-Ming Lee</creatorcontrib><creatorcontrib>Jan-Ming Ho</creatorcontrib><title>EFS: Expert Finding System based on Wikipedia link pattern analysis</title><title>2008 IEEE International Conference on Systems, Man and Cybernetics</title><addtitle>ICSMC</addtitle><description>Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia Web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like ldquoArtificial Intelligencerdquo and ldquoImage & Pattern Recognitionrdquo etc.</description><subject>Application software</subject><subject>Automatic Term Recognition</subject><subject>Computer science</subject><subject>Electronic mail</subject><subject>Expert Finding</subject><subject>Information science</subject><subject>Ontologies</subject><subject>Pattern analysis</subject><subject>Proposals</subject><subject>Social network services</subject><subject>Wikipedia</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>142442383X</isbn><isbn>9781424423835</isbn><isbn>1424423848</isbn><isbn>9781424423842</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkM1OhDAURuvPJMLoC-imLwD2tqUt7gxhdJIxLtA4u0mBi6nDIKEshrcX4ySuvsU5OYuPkFtgMQBL79dZ8ZLFnDETSwMgpDkjIUguJRdGmnMS8ETrCFSSXPwDsb0kATDFo5Tz7YKEv4GUibl4RULvvxjjTIIJSJavigeaH3scRrpyXe26T1pMfsQDLa3Hmn539MPtXY-1s7R13Z72dhxx6KjtbDt556_JorGtx5vTLsn7Kn_LnqPN69M6e9xEjkM6RpUUaBuLmjWGm4ZjomyVyhJ5qqEEUCrBEpRtKhCzWyVCV2ANCj3bgpViSe7-ug4Rd_3gDnaYdqdbxA89G1Aa</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Kai-Hsiang Yang</creator><creator>Chun-Yu Chen</creator><creator>Hahn-Ming Lee</creator><creator>Jan-Ming Ho</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20080101</creationdate><title>EFS: Expert Finding System based on Wikipedia link pattern analysis</title><author>Kai-Hsiang Yang ; Chun-Yu Chen ; Hahn-Ming Lee ; Jan-Ming Ho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i219t-c43eafae70f828f2e56ac94be2971b11665eb16afc13c43c537c1a8e3728f30b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Application software</topic><topic>Automatic Term Recognition</topic><topic>Computer science</topic><topic>Electronic mail</topic><topic>Expert Finding</topic><topic>Information science</topic><topic>Ontologies</topic><topic>Pattern analysis</topic><topic>Proposals</topic><topic>Social network services</topic><topic>Wikipedia</topic><toplevel>online_resources</toplevel><creatorcontrib>Kai-Hsiang Yang</creatorcontrib><creatorcontrib>Chun-Yu Chen</creatorcontrib><creatorcontrib>Hahn-Ming Lee</creatorcontrib><creatorcontrib>Jan-Ming Ho</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kai-Hsiang Yang</au><au>Chun-Yu Chen</au><au>Hahn-Ming Lee</au><au>Jan-Ming Ho</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>EFS: Expert Finding System based on Wikipedia link pattern analysis</atitle><btitle>2008 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2008-01-01</date><risdate>2008</risdate><spage>631</spage><epage>635</epage><pages>631-635</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>142442383X</isbn><isbn>9781424423835</isbn><eisbn>1424423848</eisbn><eisbn>9781424423842</eisbn><abstract>Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia Web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like ldquoArtificial Intelligencerdquo and ldquoImage & Pattern Recognitionrdquo etc.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2008.4811348</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1062-922X |
| ispartof | 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, p.631-635 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_4811348 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Automatic Term Recognition Computer science Electronic mail Expert Finding Information science Ontologies Pattern analysis Proposals Social network services Wikipedia |
| title | EFS: Expert Finding System based on Wikipedia link pattern analysis |
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