Efficient Application of Complex Graph Analytics on Very Large Real World RDF Datasets.
Zhe Wu, Jay Banerjee · International Semantic Web Conference · 2014
RDF [1] Graph modeling is a foundational technology in the whole semantic web (SW) technology stack. Since its debut in 2004, RDF graph has enjoyed many applications in the enterprise domain. Examples of these applications include, but certainly not limited to, integration and federated query of heterogeneous data sources, flexible and extensible representation of enterprise knowledge base, adhoc query and navigation on top of schema-less graph model of enterprise data, social network representation and link analysis, and metadata processing in the context of master data management (MDM). In the past decade, many mature open source and commercial RDF platforms and solutions [6] have been developed to store and index RDF graph data (triples and quads), edit and manage OWL [2] ontologies, perform logical inference, execute pattern matching and graph navigation (SPARQL [3]), visualize RDF graph data and OWL ontologies, and link data in RDF format and also other data types including relational (RDB2RDF [4,5]). As a graph modeling language, RDF provides great flexibility for enterprise applications and it adds precision, through the use of URI and formal semantics, to enterprise data. SPARQL query and OWL inference have been two key functions for semantic web applications. A somewhat less obvious application of RDF is that such a graph model is also a great candidate for graph analytics.