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Using Rank Aggregation for Expert Search in Academic Digital Libraries

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Catarina Moreira, Bruno Martins, Pável Calado

Information Retrieval · Submitted 21 Jan 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.

how it is stated · asserted 0.95 read the record →

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arXiv ID1501.05140 (version 2)
Pith IDUL7P5YUY, Pith's permanent record of this paper and its review
CitationBruno Martins, Catarina Moreira, P\'avel Calado. "Using Rank Aggregation for Expert Search in Academic Digital Libraries." arXiv:1501.05140 (2015). https://pith.science/paper/UL7P5YUY
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,243 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.13 · human participants 0.06 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.15 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The task of expert finding has been getting increasing attention in information retrieval literature. background 1.00 0.04 0.02 asserted 1.00
s1 However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. gap 1.00 0.07 0.02 asserted 1.00
s2 This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. contribution 0.92 0.74 0.18 asserted 0.73
s3 We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. method 0.95 0.94 0.04 asserted 0.97
s4 Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods. result 0.92 0.94 0.69 asserted 0.95

What kind of paper the classifier reports this is: empirical study 0.56 · evidence from observation or experiment 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.

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