The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Jagannathan Srinivasan - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
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implementing an inference engine for rdfs owl constructs and user defined rules in oracle
International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Souripriya Das, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
Robert John - One of the best experts on this subject based on the ideXlab platform.
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FUZZ-IEEE - Optimised Generalised Type-2 Join and Meet Operations
2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Sarah Greenfield, Robert JohnAbstract:The Inferencing stage of a type-2 fuzzy Inferencing system is driven by join and meet operations. As conventionally implemented these algorithms are computationally complex. This article introduces optimised implementations for these operations. These alternative procedures pre-suppose that the grid method of discretisation is adopted. Time comparisons between the alternative and conventional implementations have been undertaken, for join and meet operations coded firstly in isolation, and secondly, within a full prototype type-2 fuzzy Inferencing system. The experimental results show that the optimisation affords marked time reduction, particularly under finer discretisation.
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Type 2 fuzzy sets for knowledge representation and Inferencing
1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1998Co-Authors: Robert JohnAbstract:Type 2 fuzzy sets allow for linguistic grades of membership thus assisting in knowledge representation. They also offer improvement on Inferencing with type 1 sets. The various approaches to knowledge representation and Inferencing are discussed, with worked examples, and some of the applications of type 2 sets are reported.
Zhe Wu - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
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implementing an inference engine for rdfs owl constructs and user defined rules in oracle
International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Souripriya Das, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
Vladimir Kolovski - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
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implementing an inference engine for rdfs owl constructs and user defined rules in oracle
International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Souripriya Das, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
Mani Annamalai - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
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implementing an inference engine for rdfs owl constructs and user defined rules in oracle
International Conference on Data Engineering, 2008Co-Authors: Zhe Wu, George Eadon, Souripriya Das, Eugene Inseok Chong, Vladimir Kolovski, Mani Annamalai, Jagannathan SrinivasanAbstract:This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) Inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the Inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.
