We present a method for constructing synthetic datasets of Competency Questions translated into SPARQL-OWL queries. This method is used to generate BigCQ, the largest set of CQ patterns and SPARQL-OWL templates that can provide translation examples to automate assessing the completeness and correctness of ontologies.

Ontology authoring is a complicated and error-prone process since the knowledge being modeled is expressed using logic-based formalisms, in which logical consequences of the knowledge have to be foreseen. To make that process easier, competency questions (CQs), being questions expressed in natural language are often stated to trace both the correctness and completeness of the ontology at a given time. However, CQs have to be translated into a formal language, like ontology query language (SPARQL-OWL), to query the ontology. Since the translation step is time-consuming and requires familiarity with the query language used, in this paper, we propose an automatic method named SeeQuery, which recommends SPARQL-OWL queries being translations of CQs stated against a given ontology. It consists of a pipeline of transformations based on template matching and filling, being motivated by the biggest to date publicly available CQ to SPARQL-OWL datasets. We provide a detailed description of SeeQuery and evaluate the method on a separate set of 2 ontologies with their CQs. It is, to date, the only automatic method available for recommending SPARQL-OWL queries out of CQs. The source code of SeeQuery is available at: https://github.com/dwisniewski/SeeQuery.

Ontology authoring is a complicated and error-prone process since the knowledge being modelled is expressed using logic-based formalisms, in which logical consequences of the knowledge have to be foreseen.Many approaches intended to make this task easier, use competency questions (CQs), being questions expressed in natural language to trace both the correctness and completeness of the ontology at a given time.However, CQs hold so-called presuppositions that have to be satisfied by the ontology to obtain meaningful answers from CQs.Moreover, CQs have to be expressed using a formal language, like ontology query language (SPARQL-OWL), to query the ontology.In this paper, we propose an extension of test-driven ontology development approach by formalization of presupposition satisfaction tests in terms of SPARQL-OWL queries, as well as providing translations of CQs into SPARQL-OWL queries if presupposition tests are passed.We provide a detailed description of the proposed framework and how to incorporate such tests in the workflow of test-driven development of ontologies.It is the first framework available for formalization of SPARQL-OWL queries out of CQs with their presupposition tests.

We present an idea of using mathematicall modelling to guide a process of mining a set of patterns in an RDF graph and further exploiting these patterns to build expressive OWL class hierarchies.

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.