RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Saleiro, P (Autor) Outra; Frayling, NM (Autor) Outra; Rodrigues, EM (Autor) Outra; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017

Páginas: 1273-1276

Indexação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00N-18Q

DOI: 10.1145/3077136.3080756

Abstract (EN): Improvements of entity-relationship (E-R) search techniques have been hampered by a lack of test collections, particularly for complex queries involving multiple entities and relationships. In this paper we describe a method for generating E-R test queries to support comprehensive E-R search experiments. Queries and relevance judgments are created from content that exists in a tabular form where columns represent entity types and the table structure implies one or more relationships among the entities. Editorial work involves creating natural language queries based on relationships represented by the entries in the table. We have publicly released the RELink test collection comprising 600 queries and relevance judgments obtained from a sample of Wikipedia List-of-lists-oflists tables. The latter comprise tuples of entities that are extracted from columns and labelled by corresponding entity types and relationships they represent. In order to facilitate research in complex E-R retrieval, we have created and released as open source the RELink Framework that includes Apache Lucene indexing and search specifically tailored to E-R retrieval. RELink includes entity and relationship indexing based on the ClueWeb-09-BWeb collection with FACC1 text span annotations linked to Wikipedia entities. With ready to use search resources and a comprehensive test collection, we support community in pursuing E-R research at scale. © 2017 ACM.

Idioma: Inglês

Tipo (Avaliação Docente): Científica