RDF Keyword Search by Query Computation

Abstract

Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.

RDF(S) Store in Object-Relational Databases

“…With the rapid increase in the number of RDF(S) on the Web, it has become increasingly important to efficiently store massive amounts of RDF(S). The storage of RDF(S) (Ma, Capretz and Yan, 2016) often supports efficient queries of RDF data, mainly because the storage structure of RDF(S) not only directly determines the integrity of storage semantics, but also greatly affects its query efficiency Ma, et al, 2018). At present, there have been many studies on RDF(S) storage methods, which can be roughly divided into the following three categories: 1) Memory-based storage (e.g., Sesame (Broekstra, et al, 2002) and BitMat (Atre, et al, 2008)).…”

Contributions to the Problem of Keyword Search Over Datasets and Semantic Trajectories Based on the Resource Description Framework

“…al. [39] described a keywords-to-SPARQL translation process that circumvents the lack of underlying schema information. They compute, from the RDF data graph, an inter-entity relationship summary with complete schema information, and adopt a search prioritization scheme that combines the degree of a vertex with the distance from the original keyword element.…”