First-order logic and automated theorem proving (2nd ed.)
First-order logic and automated theorem proving (2nd ed.)
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
The foundational model of anatomy in OWL: Experience and perspectives
Web Semantics: Science, Services and Agents on the World Wide Web
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family
Journal of Automated Reasoning
Data Complexity of Query Answering in Expressive Description Logics via Tableaux
Journal of Automated Reasoning
Web Semantics: Science, Services and Agents on the World Wide Web
Combining a DL Reasoner and a Rule Engine for Improving Entailment-Based OWL Reasoning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Modular reuse of ontologies: theory and practice
Journal of Artificial Intelligence Research
Conjunctive query answering for the description logic SHIQ
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Conjunctive query answering in the description logic EL using a relational database system
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Efficient Query Answering for OWL 2
ISWC '09 Proceedings of the 8th International Semantic Web Conference
The DL-lite family and relations
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
Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
OWLIM: A family of scalable semantic repositories
Semantic Web
Towards a complete OWL ontology benchmark
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
OWLIM – a pragmatic semantic repository for OWL
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
Query rewriting under ontology contraction
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
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To achieve scalability of query answering, the developers of Semantic Web applications are often forced to use incomplete OWL 2 reasoners, which fail to derive all answers for at least one query, ontology, and data set. The lack of completeness guarantees, however, may be unacceptable for applications in areas such as health care and defence, where missing answers can adversely affect the application's functionality. Furthermore, even if an application can tolerate some level of incompleteness, it is often advantageous to estimate how many and what kind of answers are being lost. In this paper, we present a novel logic-based framework that allows one to check whether a reasoner is complete for a given query Q and ontology T -- that is, whether the reasoner is guaranteed to compute all answers to Q w.r.t. T and an arbitrary data set A. Since ontologies and typical queries are often fixed at application design time, our approach allows application developers to check whether a reasoner known to be incomplete in general is actually complete for the kinds of input relevant for the application. We also present a technique that, given a query Q, an ontology T, and reasoners R1 and R2 that satisfy certain assumptions, can be used to determine whether, for each data set A, reasoner R1 computes more answers to Q w.r.t. T and A than reasoner R2. This allows application developers to select the reasoner that provides the highest degree of completeness for Q and T that is compatible with the application's scalability requirements. Our results thus provide a theoretical and practical foundation for the design of future ontology-based information systems that maximise scalability while minimising or even eliminating incompleteness of query answers.