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Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap (Property Graph to Ontology Mapper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

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The evolution of heterogeneous data residing in various data sources (e.g., relational, XML, document stores, etc.) increases the data integration challenges. Ontology Based Data Access (OBDA) is a semantic web technology that comprises a set of algorithms and techniques for dealing with data heterogeneity. Ontologies are utilized in OBDA to provide a conceptual view over diverse datasets; and the relationships between them are defined through mappings in two ways: data translation, and query translation. The first method is referred to as materialization, where data transformation is achieved in accordance with the global view. Whereas in the second method, query transformation is carried out from the query language of the global schema into the original data source's query language. In this paper, we present the framework of OBDA by discussing the main components of ontology-based data access, techniques, applications and future challenges.