Increasing amounts of scienti c and social data are published in the Resource Description Framework (RDF). Although the RDF data can be queried using the SPARQL language, even the SPARQL-based operation has a limitation in implementing traversal or analytical algorithms. Recently, a variety of graph database implementations dedicated to analyses on the property graph model have emerged. However, the RDF model and the property graph model are not interoperable. Here, we developed a framework based on the Graph to Graph Mapping Language (G2GML) for mapping RDF graphs to property graphs to make the most of accumulated RDF data. Using this framework, graph data described in the RDF model can be converted to the property graph model and can be loaded to several graph database engines for further analysis. Future works include implementing and utilizing graph algorithms to make the most of the accumulated data in various analytical engines.
Increasing amounts of scienti c and social data are described as graphs. As a format of graph data, the Resource Description Framework (RDF) is widely used. Although RDF data can be queried using the SPARQL language in a exible way, SPARQL is not dedicated to traversal of graphs and has a limitation in implementing graph analysis algorithms.
In the context of graph analysis, the property graph model [
Considering this situation, it is valuable to develop a method to transform
RDF data into property graphs. However, the transformation is not
straightforward due to the di erences in the data model. In RDF graphs, all information is
expressed as the triple (node-edge-node), whereas in property graphs, arbitrary
information can be contained in each of the nodes and edges as key-value form.
Although previous works addressed this issue by formalizing transformations [
Here, we developed a framework based on the Graph to Graph Mapping Language (G2GML) for mapping RDF graphs to property graphs. Using this framework, accumulated graph data described in the RDF model can be converted to the property graph model and can be loaded to several graph database engines. 2
that one musician and another are in the same group, the information can be summarized into the property graph data as shown in this gure.
For this conversion, the actual G2GML is described as in Figure 3. It starts with URI pre xes used to write mappings, and then, each mapping consists of one unindented line of a property graph pattern and indented lines of an RDF graph pattern. A property graph pattern is written in a syntax like Cypher (the query language of Neo4j), whereas an RDF graph pattern is written as a pattern in SPARQL. Variables in each pattern are mapped by those names. This example contains one node mapping for Musician entity and one edge mapping for same group relationship only. In G2GML, edge mappings are de ned based on the conditions of node mappings, which means that edges are generated in property graph i both nodes' patterns and edges' patterns are matched in RDF graph. Also, mus, nam, dat, twn and len are used as variables to extract resources and literals from RDF graph. In the resulting property graph, resources can be mapped to nodes, while literals can be mapped to values of properties.
Finally, Figure 4 shows the SPARQL query to retrieve the pairs of musicians who are in the same group. After G2GML mapping above, we can load the generated property graph data into graph databases, such as Oracle Labs PGX, and the query can be written in PGQL (the query language of PGX). 4
In this work, we de ned G2GML for mapping RDF graphs to property graphs and implemented a converter based on the G2GML. We also showed an example usage of G2GML. Future works include further analysis of the converted graph data on the database engines adopting the property graph model. # Node mapping (mus:Musician {vis_label:nam, born:dat, hometown:twn}) ?mus rdf:type foaf:Person, dbpedia-owl:MusicalArtist . ?mus rdfs:label ?nam .
OPTIONAL { ?mus prop:born ?dat }
OPTIONAL { ?mus dbpedia-owl:hometown / rdfs:label ?twn } # Edge mapping (mus1:Musician)-[:same_group {label:nam, length:len}]->(mus2:Musician) ?grp a schema:MusicGroup ;
dbpedia-owl:bandMember ?mus1 , ?mus2 .
FILTER(?mus1 != ?mus2) OPTIONAL { ?grp rdfs:label ?nam. FILTER(lang(?nam) = "ja")} OPTIONAL { ?grp dbpedia-owl:wikiPageLength ?len } # PGQL SELECT DISTINCT m1.name, m2.name WHERE (m1)-[same_group]-(m2) # PG Pattern # RDF Pattern # PG Pattern # RDF Pattern Modern Query Languages for Graph Databases. ACM Computing Surveys (CSUR),