2006 · 11 citations · 12 references
ComputingComputer ScienceInformation TechnologyData ManagementDatabasesKnowledge RepresentationData IntegrationData ModelingDatabase ManagementQuery ProcessingQuestion AnsweringStructured Query LanguageHeterogeneityKnowledge Base ConstructionQuery AnsweringSemantic Data ManagementSemantic Web
We investigate the challenges that must be addressed for the Semantic Web to become a feasible enterprise. Specifically we focus on the query answering capability of the Semantic Web. We put forward that two key challenges we face are heterogeneity and scalability. We propose a flexible and de-centralized framework for addressing the heterogeneity prob-lem and demonstrate that sufficient reasoning is possible over a large dataset by taking advantage of database technologies and making some tradeoff decisions. As a proof of concept, we collect a significant portion of the available Semantic Web data; use our framework to resolve some heterogeneity and reason over the data as one big knowledge base. In addition to demonstrating the feasibility of a “real ” Semantic Web, our experiments have provided us with some interesting insights into how it is evolving and the type of queries that can be answered.
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LUBM: A benchmark for OWL knowledge base systems
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An efficient SQL-based RDF querying scheme
Eugene Inseok Chong, Souripriya Das, George Eadon et al. · Very Large Data Bases · 2005 · 287 citations