Open AccessRDF keyword search using multiple indexes
Author(s) -
Xiaoqing Lin,
Fu Zhang,
Danling Wang,
Jingwei Cheng
Publication year - 2018
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil1805861l
Subject(s) - sparql , computer science , keyword search , rdf , rdf schema , index (typography) , vertex (graph theory) , information retrieval , schema (genetic algorithms) , named graph , graph , inverted index , theoretical computer science , search engine indexing , semantic web , world wide web
Since SPARQL has been the standard language for querying RDF data, keyword search based on keywords-to-SPARQL translation attracts more intention. However, existing keyword search based on keywords-to-SPARQL translation have limitations that the schema used for keyword-to-SPARQL translation is incomplete so that wrong or incomplete answers are returned and advantages of indexes are not fully taken. To address the issues, an inter-entity relationship summary (ER-summary) is constructed by distilling all the inter-entity relationships of RDF data graph. On ER-summary, we draw circles around each vertex with a given radius r and in the circles we build the shortest property path index (SP-index), the shortest distance index (SD-index) and the r-neighborhoods index by using dynamic programming algorithm. Rather than searching for top-k subgraphs connecting all the keywords centered directly as most existing methods do, we use these indexes to translate keyword queries into SPARQL queries to realize exchanging space for time. Extensive experiments show that our approach is efficient and effective.