A generic Web-based entity resolution framework

Abstract

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.

Tendências e perspectivas de pesquisa sobre repositórios digitais no Brasil: uma análise de Rede Sociais (ARS)

“…O estudo realizado por meio dos dados coletados na base Web of Science 31 permitiu que fosse emitida uma análise preliminar da rede percebida pela Figura 1 2,14-30,32-57 . 14,18,20,27 9,756 GUIMARÃES, MCS 15,16,17 7,317 LAENDER, AHF 18,19,20 7,317 RODRIGUES, RS 21,22,23 7,317 AMANCIO, DR 24,25 4,878 COSTA, LF 24,25 4,878 DA SILVA, AS 26,27 4,878 DE MOURA, ES 26,27 4,878 MARCONDES, CH 28,29 4,878 RIBEIRO-NETO, B 19,20 4,878 RODRIGUES, JG 15,16 4,878 SAYÃO, LF 29,30 4,878 ZIVIANI, N 20,26 4,878 14,18,20,27 14 LAENDER, AHF 18,19,20 10 ZIVIANI, N 20, 26 8 DE MOURA, ES 26,27 7 RIBEIRO-NETO, B 19,20 7 FREIRE, IM 32 5 DE AZEVEDO NETTO, C 32 5 DUARTE, E 2, 32 5 RAMALHO, FA 32 5 DIAS, GA…”

“…Pelos dados analisados, é possível perceber que os atores que mais se destacam participam igualmente do maior número de cliques. Como, por exemplo, Gonçalves, MA 14,18,20,27 , que tem a incidência em cinco cliques.…”

“…A centralidade permite saber o posicionamento do ator em relação às trocas e à comunicação na rede33 .Desse modo, percebe-se que quanto mais central o indivíduo na rede, maior o seu poder dentro dela.Uma comparação entre a Tabela 1, em que se utilizou o índice h, e a Tabela 2, em que foram usadas informações do software UCINET, baseados nos cálculos de Freeman12,13 , concede a noção de que o fato de o autor ser mais citado não implica necessariamente a ideia de ele ter mais impacto na rede. Isso pode ser explicado pelo conceito de "aberturas estruturais", ou seja, um ator que se intercambiou entre a rede em diversas relações, maximiza os seus contatos, o que emite a ele uma centralidade maior do que os demais, como averiguado, por exemplo, no caso do autor Ziviani20,26 . Na Tabela 1, ele é visto com um de menor índice de citações na área, entretanto, na análise de redes sociais, percebe-se que ele se sobressai aos demais, se tornando o terceiro com maior índice de centralidade.…”

Disambiguating publication venue titles using association rules

“…Another similar approach was investigated in [12], which used a web search engine to find additional information to help cluster publication venues. Their ideas were extended in a more generic framework, described in [13]. To create our authority file, we did not use a web search engine because our data sources were already wrapped from the main web sources.…”

“…The first one is a real dataset obtained by querying Google Scholar. This dataset was used in the works [12] and [13]. It contains 1, 110 distinct reference strings to 55 distinct publication venues, having on an average 20.2 strings per class, the largest class has 81 strings, and there are only 7 classes with only one string.…”