Expertise Retrieval

Balog, Krisztian

doi:10.1561/1500000024

Cited by 186 publications

(135 citation statements)

References 5 publications

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“…Other interpretations, such as those that cannot be answered by the knowledge base, should be less favored, everything else beging equal. The optimization is subject to the following constraints which both forbid nonsensical interpretations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), and downweigh empty interpretations of a query (13, 14):…”

Section: P(t)mentioning

confidence: 99%

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Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This paper advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user's input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questions into an extended form of structured SPARQL queries, with text predicates attached to triple patterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark.

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Section: P(t)mentioning

confidence: 99%

“…Ranking: Statistical language models (LM's) [48] have been used for ranking the results of entity search (e.g., [4,6,17,31,42]). Also random-walk-based models have been developed for this purpose (e.g., [19,38]).…”

Section: Related Workmentioning

confidence: 99%

Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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“…In this case it is reasonable to assume that his articles are more useful than those created by authors having other roles. This assumption is supported by the study about expertise retrieval [19]. According to that study, the expertise of employees is an asset and its effective sharing is crucial from a company's point of view.…”

Section: Approachmentioning

confidence: 91%

Key Performance Indicators for Evaluating Corporate Wikis for Knowledge Management

Mützel¹,

Tafreschi²

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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This paper presents a methodology for assessing and improving the quality of information provided by corporate wikis. Regarding the assessment, we present two KPIs for measuring relative demand and relative usefulness of wiki articles, including corresponding processes and data model. In regard to improving quality, we use the KPIs to classify the articles. For this classification, we introduce four categories and discuss possible actions for reducing information overload and increasing the visibility of articles. To prove our methodology, we analyze an existing corporate wiki of a large European enterprise in the chemical industry. Its articles are used to demonstrate how the proposed KPIs can contribute to knowledge management by improving the information quality.

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“…Lappas et al [17] proposed to find a team of experts by considering the team formation problem in social networks. Balog et al have done a lot of work about expertise retrieval [35], [36] and provided a good overview of expertise retrieval in [1]. All these schemes fall into the category of global expert finding by using some global databases or central server and focus on expertise evidence mining and organization.…”

Section: Related Workmentioning

confidence: 99%

Mechanism design for finding experts using locally constructed social referral web

Zhang

Huang

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90% in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20% of the untruthful methods, e.g.. the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

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Expertise Retrieval

Cited by 186 publications

References 5 publications

Robust question answering over the web of linked data

Robust question answering over the web of linked data

Key Performance Indicators for Evaluating Corporate Wikis for Knowledge Management

Mechanism design for finding experts using locally constructed social referral web

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