Keeping a digital library clean

Laender, Alberto H. F.; Gonçalves, Marcos André; Cota, Ricardo G.; Ferreira, Anderson A.; Santos, Rodrygo L. T.; Silva, Allan J. C.

doi:10.1145/1410140.1410195

Cited by 22 publications

(1 citation statement)

References 12 publications

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“…Esses valores são utilizados pelas agências para tomada de decisões, como por exemplo, conceder ou não apoio financeiro a um projeto. Entretanto, ao realizar esse tipo de análise, os usuários pressupõem que o conteúdo seja de alta qualidade [43]. Essa necessidade faz com que a imperfeição dos dados nas BDs seja considerada um problema crítico, que afeta além da eficácia, a eficiência dos serviços prestados por estes portais.…”

Section: Introductionunclassified

Uma abordagem baseada em realimentação de relevância para o problema da desambiguação de nome de autores

Godoi¹,

Carvalho²

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This work presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our disambiguation method combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming (GP) framework.The main contributions of this work are: (i) proposal of a novel author name desambiguation method; (ii) evaluation in a new application of the combination between GP and OPF algorithms, also known as GOPF, in interaction learning systems; (iii) evaluation of the GOPF algorithm in a multi-class classification problem; and (iv) extension of the GOPF algorithm to handle open-set classification problems, i.e., classification problems in which class samples are not known in advance.The proposed method was validated with two traditional databases largely used for the evaluation of author name disambiguation methods: one is a collection extracted from DBLP which sums up 4,287 references associated with 220 distinct authors; the other is called KISTI and was built by the Korea Institute of Science and Technology Information; it contains the top 1000 most frequent author names from the late-2007 DBLP database. After 5 iterations of relevance feedback, our approach yielded the best results for author name disambiguation when compared with the state-of-the-art methods that just consider basic reference information, such as author names, publication title, and venue title. xi AgradecimentosEu gostaria de agradecer . . . A Deus, pela proteção diária e também por todos os dons que me concedeu. Aos meus pais, Marcos e Catarina, que no decorrer de toda a minha vida me deram educação, carinho, amor, caráter e me incentivaram na busca de meus objetivos. A minha noiva Fabiana Thomé, que está sempre ao meu lado nos momentos de alegria e de dificuldade. Ao CNPq, ao banco Santander e à FAEPEX pelo apoio financeiro para execução deste trabalho. Aos professores Dr. Edward Fox e Dr. Patrick Fan, da Virginia Polytechnic Institute and State University, que me receberam entre agosto e novembro de 2012 e me deram valiosos conselhos e orientações para realização deste trabalho. Aos professores Dr.

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Section: Introductionunclassified

Uma abordagem baseada em realimentação de relevância para o problema da desambiguação de nome de autores

Godoi¹,

Carvalho²

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A generic Web-based entity resolution framework

Pereira

Ribeiro-Neto

Ziviani

et al. 2011

J. Am. Soc. Inf. Sci.

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Web data repositories usually contain references to thousands of real-world entities from multiple sources. It is not uncommon that multiple entities share the same label (polysemes) and that distinct label variations are associated with the same entity (synonyms), which frequently leads to ambiguous interpretations. Further, spelling variants, acronyms, abbreviated forms, and misspellings compound to worsen the problem. Solving this problem requires identifying which labels correspond to the same real-world entity, a process known as entity resolution. One approach to solve the entity resolution problem is to associate an authority identifier and a list of variant forms with each entity-a data structure known as an authority file. In this work, we propose a generic framework for implementing a method for generating authority files. Our method uses information from the Web to improve the quality of the authority file and, because of that, is referred to as WER-Web-based Entity Resolution. Our contribution here is threefold: (a) we discuss how to implement the WER framework, which is flexible and easy to adapt to new domains; (b) we run extended experimentation with our WER framework to show that it outperforms selected baselines; and (c) we compare the results of a specialized solution for author name resolution with those produced by the generic WER framework, and show that the WER results remain competitive.1. for each record do: (a) select one or more attributes and use them to compose a query, (b) submit the query to a search engine, and (c) collect the top m documents in the answer set; 2. parse the documents in the answer set and extract (Webinferred) attributes B j such as URLs, titles, texts of the documents, names, and acronyms used to refer to the entities; 3. using the original attributes A i and the Web-inferred attributes B j , cluster the entity records relative to the primary entity e pr , such that each cluster corresponds to a single real-world entity; 4. in each cluster, select an attribute value to be the canonical entity name, and output the respective entry to the authority file.

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Self‐training author name disambiguation for information scarce scenarios

Ferreira

Veloso

Gonçalves

et al. 2014

Asso for Info Science & Tech

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We present a novel 3-step self-training method for author name disambiguation-SAND (self-training associative name disambiguator)-which requires no manual labeling, no parameterization (in real-world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real-world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state-of-the-art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self-citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information.

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Keeping a digital library clean

Cited by 22 publications

References 12 publications

Uma abordagem baseada em realimentação de relevância para o problema da desambiguação de nome de autores

Uma abordagem baseada em realimentação de relevância para o problema da desambiguação de nome de autores

A generic Web-based entity resolution framework

Self‐training author name disambiguation for information scarce scenarios

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