Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query algorithms have been proposed recent years and implemented highly optimised systems. systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the more errors become likely to happen. In this paper, we present an algor...

The querying on big data is a challenging task due to the rapid growth of amount. Approximate query processing (AQP) way meet requirement fast response. In this paper, we propose learning-based AQP method called LAQP. LAQP builds an error model learned from historical queries predict sampling-based estimation each new query. It makes combination AQP, pre-computed aggregations and provide high-a...

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In this paper, we place ourselves in the context of inconsistency-tolerant query answering over lightweight ontologies, which aims to a set conflicting facts using an ontology that represents generic knowledge about particular domain. Existing semantics typically consist selecting some (maximal) consistent subsets facts, called repairs. We explore novel strategy select most relevant repairs bas...