Ranking experts using author-document-topic graphs

Gollapalli, Sujatha Das; Mitra, Prasenjit; Giles, C. Lee

doi:10.1145/2467696.2467707

Cited by 30 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By surveying different ranking methods and evaluating question and answer relevance, we can better understand how ranking can assist with CQA question and answer relationships. Gollapalli, Mitra, and Giles () rank the users with a graph‐based method, but it does not clearly demonstrate how to rank the answers based on the users. The neural network is also deployed to rank the content in CQA (Iyyer, Boyd‐Graber, Claudino, Socher, & Daumé, ; Qiu & Huang, ).…”

Section: Background and Related Workmentioning

confidence: 99%

Retrieving people: Identifying potential answerers in Community Question‐Answering

Shah

2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

Community Question-Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites -Yahoo! Answers and Stack Overflow -to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Retrieving people: Identifying potential answerers in Community Question‐Answering

Shah

2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…However, as motivated in Section 3, this simple quantitative metric as well as other standard indices based on citations counts, i.e., Hirsch index and variants [9,3,14], are not discriminative enough to identify authoritative researchers. Graph-based models, largely based on random walk [20], are often used to identify important nodes in networks and the correlation between centrality and expertise in organizational networks have been extensively studied [12,4,17,11,21,23,13,8,6]. The value of the co-citation graph has been proven for web pages [12].…”

Section: Related Workmentioning

confidence: 99%

Identifying authoritative researchers in digital libraries using external a priori knowledge

Robertie

Pitarch

Takasu

et al. 2017

Proceedings of the Symposium on Applied Computing

View full text Add to dashboard Cite

Numereous digital library projects mine heterogeneous data from different sources to provide expert finding services. However, a variety of models seek experts as simple sources of information and neglect authority signals. In this paper we address the issue of modelling the authority of researchers in academic networks. A model, RAC, is proposed that merges several graph representations and incorporate external knowledge about the authority of some major scientific conferences to improve the identification of authoritative researchers. Based on the provided structural model a biased label propagation algorithm aimed to strenghten the scores calculation of the labelled entities and their neighbors is developped. Both quantitative and qualitative analyses validate the effectiveness of the proposal. Indeed, RAC outperforms state-of-the-art models on a real-world graph containing more than 5 million nodes constructed using Microsoft Academic Search, AMiner and Core.edu databases.

show abstract

“…In organizational networks, graph-based models, largely based on random walk [23], are widely used to estimate individual authority. In this field, extensive researches have demonstrated strong correlations between centrality and authority [10,16,26,27]. The famous PageRank algorithm proposed by L. Page et al [18] and later the Topic-sensitive Pagerank [11] have proven the value of the citation graph for web pages.…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach for Learning Expertise and Authority in Digital Libraries

Robertie

Ermakova

Pitarch

et al. 2017

Database Systems for Advanced Applications

View full text Add to dashboard Cite

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18909The contribution was presented at DASFAA 2017 :http://ada.suda.edu.cn/dasfaa2017/ Abstract. Managing individual expertise is a major concern within any industrial-wide organization. If previous works have extensively studied the related expertise and authority profiling issues, they assume a semantic independence of these two key concepts. In digital libraries, stateof-the-art models generally summarize the researchers' profile by using solely textual information. Consequently, authors with a large amount of publications are mechanically fostered to the detriment of less prolific ones with probably higher expertise. To overcome this drawback we propose to merge the two representations of expertise and authority and balance the results by capturing a mutual reinforcement principle between these two notions. Based on a graph representation of the library, the expert profiling task is formulated as an optimization problem where latent expertise and authority representations are learned simultaneously, unbiasing the expertise scores of individuals with a large amount of publications. The proposal is instanciated on a public scientific bibliographic dataset where researchers' publications are considered as a source of evidence of individuals' expertise and citation relations as a source of authoritative signals. Results from our experiments conducted over the Microsoft Academic Search database demonstrate significant efficiency improvement in comparison with state-of-the-art models for the expert retrieval task.

show abstract

Ranking experts using author-document-topic graphs

Cited by 30 publications

References 25 publications

Retrieving people: Identifying potential answerers in Community Question‐Answering

Retrieving people: Identifying potential answerers in Community Question‐Answering

Identifying authoritative researchers in digital libraries using external a priori knowledge

A Unified Approach for Learning Expertise and Authority in Digital Libraries

Contact Info

Product

Resources

About