GRAPE: An Expert Review Assignment Component for Scientific Conference Management Systems

Mauro, Nicola Di; Basile, Teresa Maria Altomare; Ferilli, Stefano

doi:10.1007/11504894_109

Cited by 28 publications

(21 citation statements)

References 2 publications

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“…where all notations are previously explained. As proven earlier if the taxonomy is converted into an unordered set, by ignoring the semantic relationship between its elements, then (6) produces the very same result as the Dice's measure (2).…”

Section: This Proves Substitution (3) Is Relevant and Does Not Changementioning

confidence: 86%

“…GRAPE [2] system's fundamental assumption is to prefer topics matching approach over the reviewers' bidding one, based on the idea that they give assignments more reliability [2]. It uses reviewers' preferences just to tune the assignments.…”

Section: Fig 3 Example Of a List Of Non-weighted Abstractmentioning

confidence: 99%

“…Solving task 1 is easy. As the list of conference topics is an unordered set the most reasonable way of calculating similarity factors is by using Jaccard's index (1) or Dice's similarity measure (2).…”

Section: Existing Similarity Measures Should Be Adapted or New Ones Smentioning

confidence: 99%

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Describing papers and reviewers’ competences by taxonomy of keywords

Kalmukov

2012

COMSIS J

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This article focuses on the importance of the precise calculation of similarity factors between papers and reviewers for performing a fair and accurate automatic assignment of reviewers to papers. It suggests that papers and reviewers' competences should be described by taxonomy of keywords so that the implied hierarchical structure allows similarity measures to take into account not only the number of exactly matching keywords, but in case of non-matching ones to calculate how semantically close they are. The paper also suggests a similarity measure derived from the well-known and widely-used Dice's coefficient, but adapted in a way it could be also applied between sets whose elements are semantically related to each other (as concepts in taxonomy are). It allows a non-zero similarity factor to be accurately calculated between a paper and a reviewer even if they do not share any keyword in common.

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Section: This Proves Substitution (3) Is Relevant and Does Not Changementioning

confidence: 86%

Section: Fig 3 Example Of a List Of Non-weighted Abstractmentioning

confidence: 99%

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Describing papers and reviewers’ competences by taxonomy of keywords

Kalmukov

2012

COMSIS J

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“…Another web-based conference management system is GRAPE, based on Di Mauro et al (2005). GRAPE is notable for considering both reviewers' biddings and topical similarity under two-phases assignment process.…”

Section: Grapementioning

confidence: 99%

Paper-to-reviewer assignment, based on expertise degree of reviewers and relevance degree between reviewers and papers

Watanabe

2014

IJKWI

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Automating the process of paper-to-reviewer assignment is a difficult task to be adequately resolved. Many papers concerning the related topics have been published, but there is still scare of systematic research applications. In most real world conference management, the assignment task is carried out manually by the programme committee, lacking of intelligent assigning rules and efficient matching method. The manual assignment is not only of low efficiency but also does not guarantee to result in the best solution. Given such situation, our paper sets out to analyse the problem of reviewer selection and propose a method for automatically matching papers with reviewers. Our objective is to reduce the loads of both programme committee and reviewers and make the conference-paper assignment task effectual. In this paper, we address this issue of paper-to-reviewer assignment and propose a method to model reviewers, based on the matching degree between reviewers and papers by combining preference-based approach and topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with Hungarian algorithm.Keywords: Paper-to-reviewer assignment, Hungarian algorithm, matching degree, expertise degree, relevance degree between reviewer and paper.Reference to this paper should be made as follows: Li, X. and Watanabe, T. (2014) 'Paper-to-reviewer assignment, based on expertise degree of reviewers and relevance degree between reviewers and papers', Int.

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“…GRAPE (Global Review Assignment Processing Engine) [7], is an expert system, written in CLIPS, for solving the reviewers assignment problem, that takes advantage of both the papers content (topics) and the reviewers expertise and preferences (biddings). It could be used by exploiting, in addition to the papers topics, the reviewers expertise only, or both the reviewers expertise and biddings.…”

Section: The Papers-reviewers Assignment Phasementioning

confidence: 99%

Automatic Topics Identification for Reviewer Assignment

Ferilli

Mauro

Basile

et al. 2006

Advances in Applied Artificial Intelligence

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Abstract. Scientific conference management involves many complex and multi-faceted activities, which would make highly desirable for the organizing people to have a Web-based management system that makes some of them a little easier to carry out. One of such activities is the assignment of submitted papers to suitable reviewers, involving the authors, the reviewers and the conference chair. Authors that submit the papers usually must fill a form with paper title, abstract and a set of conference topics that fit their submission subject. Reviewers are required to register and declare their expertise on the conference topics (among other things). Finally, the conference chair has to carry out the review assignment taking into account the information provided by both the authors (about their paper) and the reviewers (about their competencies). Thus, all this subtasks needed for the assignment are currently carried out manually by the actors. While this can be just boring in the case of authors and reviewers, in case of conference chair the task is also very complex and time-consuming. In this paper we proposes the exploitation of intelligent techniques to automatically extract paper topics from their title and abstract, and the expertise of the reviewers from the titles of their publications available on the Internet. Successively, such a knowledge is exploited by an expert system able to automatically perform the assignments. The proposed methods were evaluated on a real conference dataset obtaining good results when compared to handmade ones, both in terms of quality and user-satisfaction of the assignments, and for reduction in execution time with respect to the case of humans performing the same process.

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GRAPE: An Expert Review Assignment Component for Scientific Conference Management Systems

Cited by 28 publications

References 2 publications

Describing papers and reviewers’ competences by taxonomy of keywords

Describing papers and reviewers’ competences by taxonomy of keywords

Paper-to-reviewer assignment, based on expertise degree of reviewers and relevance degree between reviewers and papers

Automatic Topics Identification for Reviewer Assignment

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