Open AccessQuery Answer Reformulation over Knowledge Bases
Author(s) -
João Pedro Valladão Pinheiro,
Marco A. Casanova,
Elisa Menendez
Publication year - 2021
Publication title -
journal of information and data management
Language(s) - English
Resource type - Journals
ISSN - 2178-7107
DOI - 10.5753/jidm.2021.1914
Subject(s) - computer science , rdf , information retrieval , heuristics , knowledge base , task (project management) , linked data , context (archaeology) , sparql , query optimization , web query classification , query language , process (computing) , query expansion , rdf query language , web search query , semantic web , world wide web , search engine , economics , operating system , paleontology , management , biology
The answer of a query, submitted to a database or a knowledge base, is often long and may contain redundant data. The user is frequently forced to browse through a long answer or refine and repeat the query until the answer reaches a manageable size. Without proper treatment, consuming the answer may indeed become a tedious task. This article then proposes a process that modifies the presentation of a query answer to improve the quality of the user’s experience in the context of an RDF knowledge base. The process reorganizes the original query answer by applying heuristics to summarize the results and to select template questions that create a user dialog that guides the presentation of the results. The article also includes experiments based on RDF versions of MusicBrainz, enriched with DBpedia data, and IMDb, each with over 200 million RDF triples. The experiments use sample queries from well-known benchmarks.