Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
The Description Logic Handbook
The Description Logic Handbook
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Optimizing Terminological Reasoning for Expressive Description Logics
Journal of Automated Reasoning
SPARQL basic graph pattern optimization using selectivity estimation
Proceedings of the 17th international conference on World Wide Web
On the Scalability of Description Logic Instance Retrieval
Journal of Automated Reasoning
Conjunctive query answering for the description logic SHIQ
Journal of Artificial Intelligence Research
Hypertableau reasoning for description logics
Journal of Artificial Intelligence Research
LUBM: A benchmark for OWL knowledge base systems
Web Semantics: Science, Services and Agents on the World Wide Web
SPARQL query answering over OWL ontologies
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Towards a complete OWL ontology benchmark
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
A novel approach to ontology classification
Web Semantics: Science, Services and Agents on the World Wide Web
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The paper presents an approach for cost-based query planning for SPARQL queries issued over an OWL ontology using the OWL Direct Semantics entailment regime of SPARQL 1.1. The costs are based on information about the instances of classes and properties that are extracted from a model abstraction built by an OWL reasoner. A static and a dynamic algorithm are presented which use these costs to find optimal or near optimal execution orders for the atoms of a query. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to non-deterministic reasoning decisions.