Each new development allows loading more data, dealing with more comprehensive schemata and ontologies, and answering more complex queries in less time
As in mountain climbing , each new achievement opens new opportunities and challenges
Semantic repositories can also be seen as track-laying machines, which extend the reach of the data railways, step by step, changing the data-economy of entire domains and areas, by allowing more and more complex data to be handled at lower cost
Semantic Repositories = Track-laying machines Mar, 2010 # Semantic Repositories
We build upon lightweight semantics that is easy to understand, deploy, and manage
For instance, think of ontologies as database schemata with simple interpretation rules. Plenty of obvious (but useful) implicit facts can be inferred and match queries right away
Semantic Repositories Mar, 2010 # Semantic Repositories
It is simple Mar, 2010 # Semantic Repositories rdfs:subClassOf rdfs:subClassOf rdf: type rdf: type rdf: type rdf: type rdf: type rdf: type myData: Maria ptop:childOf rdfs:subClassOf ptop:Agent ptop:Person ptop:Woman ptop:childOf ptop:parentOf rdfs:range owl:inverseOf inferred ptop:parentOf myData:Ivan owl:relativeOf owl:inverseOf owl:SymmetricProperty rdfs:subPropertyOf ptop:relativeOf owl:inverseOf owl:inverseOf
Get more facts – Match more queries Mar, 2010 # Semantic Repositories rdfs:subClassOf rdfs:subClassOf
rdf: type rdf: type rdf: type rdf: type rdf: type rdf: type
The database will return Ivan as result of query for
RDF databases and column stores share a lot of design principles, a typical column store differs from an RDF-based semantic repository in several ways:
Globally unique identifiers. An important feature of RDF, as data representation model, is that it is based on the notion of Unique Resource Identifiers (URI)
Standard compliance. While there are no well-established standards in the area of the column stores, the RDF-based semantic repositories are highly interoperable between one another on the basis of a whole ecosystem of languages for schema definition, ontology definition, and querying
Semantic repositories can be described as “RDF-based column-stores with inference capabilities” .
Semantic Repositories vs. Column Stores Mar, 2010 # Semantic Repositories
including query preparation and optimization and fetching
which may involve changes to the ontologies and the schemata
Inference is not a first-level activity
Depending on the implementation, it can affect the performance of the other activities. In the current implementation of the data layer, inference is performed during loading and affects its performance.
whether and how complex backward-chaining is involved, whether it is recursive, etc.
Size of the result-set
fetching large result-sets can take considerable time
the number of the constraints (e.g. triple-pattern joins), the semantics of the query (e.g. negation- and disjunction-related clauses), the usage of operators that are tough to support through indexing (e.g. LIKE)
We call full-cycle benchmarking any methodology that provides a complete picture about the performance with respect to the full “life cycle” of the data within the engine
At the high-level this means publication of data for both loading and query evaluation performance in the framework of a single experiment or benchmark run.
Full-cycle benchmarking requires load performance data (e.g. “5 billion triples of LUBM were loaded in 30 hours”) to be matched with query evaluation data (e.g. “… and the evaluation of the 14 queries took 1 hour on warm database.”)