Knowledge discovery using SPARQL property path: The case of disease data set

Abstract

The Semantic Web allows knowledge discovery on graph-based data sets and facilitates answering complex queries that are extremely difficult to achieve using traditional database approaches. Intuitively, the Semantic Web query language (SPARQL) has a ‘property path’ feature that enables knowledge discovery in a knowledgebase using its reasoning engine. In this article, we utilise the property path of SPARQL and the other Semantic Web technologies to answer sophisticated queries posed over a disease data set. To this aim, we transform data from a disease web portal to a graph-based data set by designing an ontology, present a template to define the queries and provide a set of conjunctive queries on the data set. We illustrate how the reasoning engine of ‘property path’ feature of SPARQL can retrieve the results from the designed knowledgebase. The results of this study were verified by two domain experts as well as authors’ manual exploration on the disease web portal.

Finite Entailment of UCRPQs over ALC Ontologies

“…From a usability perspective, the suitability of this assumption depends on the potential applications. A particular interest for navigational queries comes from bioinformatics and cheminformatics (Lysenko et al 2016;Cook et al 2016;Galgonek et al 2016;Hu, Qiu, and Dumontier 2015;Rajabi and Sanchez-Alonso 2021;Chen et al 2020). For instance, experts often need to find associations between entities in protein, cellular, drug, and disease networks (represented as graph databases), so that e.g.…”