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- item-6 hasContent johanna-voelker.
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- 273 creator markus-kroetzsch.
- 273 creator pascal-hitzler.
- 273 creator sebastian-rudolph.
- 273 type InProceedings.
- 273 label "Description Logic Reasoning with Decision Diagrams: Compiling SHIQ to Disjunctive Datalog".
- 273 sameAs 273.
- 273 abstract "We propose a novel method for reasoning in the description logic SHIQ. After a satisfiability preserving transformation from SHIQ to the description logic ALCIb, the obtained ALCIb Tbox T is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for T. This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries.".
- 273 hasAuthorList authorList.
- 273 hasTopic Semantic_Web.
- 273 isPartOf proceedings.
- 273 keyword "SHIQ".
- 273 keyword "description logics".
- 273 keyword "disjunctive datalog".
- 273 keyword "ordered binary decision diagrams".
- 273 keyword "reasoning".
- 273 title "Description Logic Reasoning with Decision Diagrams: Compiling SHIQ to Disjunctive Datalog".
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- 322 creator alan-rector.
- 322 creator bijan-parsia.
- 322 creator colin-puleston.
- 322 creator james-cunningham.
- 322 type InProceedings.
- 322 label "Integrating Object-Oriented and Ontological Representations: A Case Study in Java and OWL".
- 322 sameAs 322.
- 322 abstract "The Web Ontology Language (OWL) provides a modelling paradigm that is especially well suited for developing models of large, structurally complex domains such as those found in Health Care and the Life Sciences. OWL's declarative nature combined with powerful reasoning tools has effectively supported the development of very large and complex anatomy, disease, and clinical ontologies. OWL, however, is not a programming language, so using these models in applications necessitates both a technical means of integrating OWL models with programs and considerable methodological sophistication in knowing how to integrate them. In this paper, we present an analytical framework for evaluating various OWL-Java combination approaches. We have developed a software framework for what we call hybrid modelling, that is, building models in which part of the model exists and is developed directly in Java and part of the model exists and is developed directly in OWL. We analyse the advantages and disadvantages of hybrid modelling both in comparison to other approaches and by means of a case study of a large medical records system.".
- 322 hasAuthorList authorList.
- 322 hasTopic Semantic_Web.
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- 322 keyword "Hybrid Modelling".
- 322 keyword "Ontology Driven Applications".
- 322 title "Integrating Object-Oriented and Ontological Representations: A Case Study in Java and OWL".
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- 328 creator claudia-damato.
- 328 creator floriana-esposito.
- 328 creator nicola-fanizzi.
- 328 type InProceedings.
- 328 label "Statistical Learning for Inductive Query Answering on OWL Ontologies".
- 328 sameAs 328.
- 328 abstract "A family of parametric language-independent kernel functions defined for individuals in OWL ontologies is presented. These functions enable the exploitation of statistical learning for various ontology mining tasks. Namely, the novel kernel functions are integrated with a support vector machine for inducing simple mathematical models that can used for performing classification through an alternative mode w.r.t. logical reasoning. Besides we also present a method for adapting the kernel to the knowledge base through stochastic optimization. An inductive approach based on machine learning may be useful especially to cope with the inherent incompleteness of the knowledge bases. The system implementing the whole framework has been tested in some experiments on query answering with real ontologies drawn from standard repositories.".
- 328 hasAuthorList authorList.
- 328 hasTopic Semantic_Web.
- 328 isPartOf proceedings.
- 328 keyword "Approximate classification".
- 328 keyword "kernel methods".
- 328 keyword "knowledge discovery".
- 328 keyword "ontology mining".
- 328 keyword "statistical learning".
- 328 title "Statistical Learning for Inductive Query Answering on OWL Ontologies".
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- item-1 hasContent nicola-fanizzi.
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