A semantic repository is a database management system. It allows storing, querying, and managing structured data. Semantic repositories is still not a largely adopted term and is often referred to by synonyms such as semantic graph database, reasoner, ontology server, semantic store, metadata store, RDF database, RDF triplestore and more. Different wording often emphasizes the particular features and usages, rather than a difference in the implementation and performance.
The major benefit of semantic repositories, compared to traditional DBMSs such as relational databases (RDBMSs), is the usage of semantic data schema paradigm, called ontology, which is stored and managed independently from the data. It allows you to:
- change the data schema “on the fly” without interfering with the data;
- automatically discover new facts and build new data based on semantic rules (data inference or reasoning);
- seamlessly integrate data from distributed data sets and data sources (data federation);
- see your data as flexible, interconnected, interlinked graph data models.
As a result, semantic repositories offer easier data integration of diverse sources as well as more analytical power.
Over the last decade, the Semantic Web emerged as an area where the semantic repository systems became as important as the HTTP servers. This tendency led to very high interest and activity in the field and resulted in a number of robust metadata, ontology and query language standards, delivered by the W3C-driven community processes, most notable among which are RDF(S), OWL and SPARQL.
The W3C standard for query language – SPARQL – has a role similar to the role SQL played in the development and spreading of RDBMSs.
Although primarily designed for use in the Semantic Web, these standards have been widely accepted in areas such as Enterprise Data Integration, Linked Open Data, Semantic Search, Knowledge Discovery, etc.
GraphDB™ is one of the most popular semantic repositories that supports W3C standards and all major syntaxes and query languages related to it. It implements and extends the open source engine rdf4j, adding efficient operational features such as inferencing, visual data exploration and database management as well as enterprise features such as high availability, distribution, consistency checks, etc.
GraphDB™ is implemented on top of the TRREE engine (TRREE stands for Triple Reasoning and Rule Entailment Engine) to combine RDFS, OWL DLP and OWL Horst support with high-performance reasoning and reliable persistence strategy. This engine performs reasoning based on forward-chaining of entailment rules over RDF triple patterns with variables and as such is optimized for very fast and computationally efficient querying. As the engine’s reasoning strategy is total materialization, various optimizations are used to ensure not only faster repository setup but also lower resource requirements and truth maintenance.
Often RDF triplestores are criticized because they don’t allow for descriptions or properties to be attached to the edges in the graph (when a set of triples are joined together, they they form a natural graph, where the predicates are interpreted as edges, and the subjects and objects are the nodes). This is perceived by some as a disadvantage compared to Property Graphs. However, this concern has been addressed with RDF-Star (abbreviated RDF*), which allows one to make statements about other statements and this way to attach metadata to the edges in the graph. As of GraphDB 9.2, Ontotext’s leading database supports RDF-Star for better expressivity.
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