A framework for the parallel processing of Datalog queries
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Data Partition and Parallel Evaluation of Datalog Programs
IEEE Transactions on Knowledge and Data Engineering
The Description Logic Handbook
The Description Logic Handbook
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Parallel Inferencing for OWL Knowledge Bases
ICPP '08 Proceedings of the 2008 37th International Conference on Parallel Processing
Island Reasoning for ALCHI Ontologies
Proceedings of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008)
Scalable Distributed Reasoning Using MapReduce
ISWC '09 Proceedings of the 8th International Semantic Web Conference
LUBM: A benchmark for OWL knowledge base systems
Web Semantics: Science, Services and Agents on the World Wide Web
The summary abox: cutting ontologies down to size
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
| Hi-index | 0.00 |
Scalability of reasoning systems is one of the main criteria which will determine the success of Semantic Web systems in the future The focus of recent work is either on (a) systems which rely on in-memory structures or (b) not so expressive ontology languages, which can be dealt with by using database technologies. In this paper we introduce a method to perform query answering for semi-expressive ontologies without the limit of in-memory structures Our main idea is to compute small and characteristic representations of the assertional part of the input ontology Query answering is then more efficiently performed over a reduced set of these small represenations We show that query answering can be distributed in a network of description logic reasoning systems to scale for reasoning Our initial results are encouraging.