Benchmarking RDF Schemas for the Semantic Web
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Artificial Intelligence in Medicine
Scalable Distributed Reasoning Using MapReduce
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Parallel Materialization of the Finite RDFS Closure for Hundreds of Millions of Triples
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
Sindice.com: weaving the open linked data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Using ontology databases for scalable query answering, inconsistency detection, and data integration
Journal of Intelligent Information Systems
Rapid benchmarking for semantic web knowledge base systems
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Towards a complete OWL ontology benchmark
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
A survey of the web ontology landscape
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
NCBO Resource Index: Ontology-based search and mining of biomedical resources
Web Semantics: Science, Services and Agents on the World Wide Web
Enabling enrichment analysis with the Human Disease Ontology
Journal of Biomedical Informatics
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As knowledge bases move into the landscape of larger ontologies and have terabytes of related data, we must work on optimizing the performance of our tools. We are easily tempted to buy bigger machines or to fill rooms with armies of little ones to address the scalability problem. Yet, careful analysis and evaluation of the characteristics of our data--using metrics--often leads to dramatic improvements in performance. Firstly, are current scalable systems scalable enough? We found that for large or deep ontologies (some as large as 500,000 classes) it is hard to say because benchmarks obscure the load-time costs for materialization. Therefore, to expose those costs, we have synthesized a set of more representative ontologies. Secondly, in designing for scalability, how do we manage knowledge over time? By optimizing for data distribution and ontology evolution, we have reduced the population time, including materialization, for the NCBO Resource Index, a knowledge base of 16.4 billion annotations linking 2.4 million terms from 200 ontologies to 3.5 million data elements, from one week to less than one hour for one of the large datasets on the same machine.