Add to ORKGAbstract. We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OWL RL Knowledge Bases in centralised, main-memory, multi-core RDF systems. Our approach comprises a datalog reasoning algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly ’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well so, with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...
Abstract. The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to int...
The Web Ontology Language (OWL) is a widely used knowledge representation language for describing kn...
Abstract. We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OW...
We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OWL RL Knowl...
We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog prog...
Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of ...
We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based ...
Abstract. OWL 2 RL was standardized as a less expressive but scalable subset of OWL 2 that allows a ...
One of the main advantages of using semantically annotated data is that machines can reason on it, d...
Abstract. One of the main advantages of using semantically annotated data is that machines can reaso...
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by ...
The size and growth rate of the Semantic Web call for querying and reasoning methods that can be app...
The large amount of Semantic Web data and its fast growth pose a significant computational challenge...
Abstract. We introduce the design of a fully parallel framework for quickly ana-lyzing large-scale R...
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...
Abstract. The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to int...
The Web Ontology Language (OWL) is a widely used knowledge representation language for describing kn...
Abstract. We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OW...
We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OWL RL Knowl...
We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog prog...
Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of ...
We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based ...
Abstract. OWL 2 RL was standardized as a less expressive but scalable subset of OWL 2 that allows a ...
One of the main advantages of using semantically annotated data is that machines can reason on it, d...
Abstract. One of the main advantages of using semantically annotated data is that machines can reaso...
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by ...
The size and growth rate of the Semantic Web call for querying and reasoning methods that can be app...
The large amount of Semantic Web data and its fast growth pose a significant computational challenge...
Abstract. We introduce the design of a fully parallel framework for quickly ana-lyzing large-scale R...
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...
Abstract. The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to int...
The Web Ontology Language (OWL) is a widely used knowledge representation language for describing kn...