Abstract
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 ( Pro perty G raph to O ntology M apper) 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.
Highlights
Property Graph databases (PGs) are extensively used in different domains e.g., social networks and web applications because of their scalability, persistent data, flexible schemata, etc
Information about correspondences between putative ontology and PG is stored separately to be used during alignment and mapping generation
This paper presents a novel approach that automatically generates mappings from property graph (PG) data sources to ontologies by addressing various mapping challenges
Summary
Property Graph databases (PGs) are extensively used in different domains e.g., social networks and web applications because of their scalability, persistent data, flexible schemata, etc. Mappings describe how different parts of the data source model correspond to different concepts of the ontology They are defined based on one of two approaches; global-as-view (GAV), or local-as-view (LAV) [3]. In LAV approach, the local data source is expressed from the global schema perspective These mappings are expressed in a standard language i.e. R2RML, for relational-to-RDF [4] or xR2RML, for non-relational-toRDF [5]. A novel ProGOMap system (Property Graph to Ontology Mapper) is proposed It automatically generates mappings from NoSQL property graphs to an existing domain ontology expressed in OWL. The main contributions of this paper include: ▪ Proposing an ontology learning method that infers class hierarchies from a property graph database, considering different modelling patterns (Algorithm). ▪ Generating PG-to-Ontology mappings from the aligned axioms and enriching them with inferred data/object properties.
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