The Experts below are selected from a list of 22023 Experts worldwide ranked by ideXlab platform
Georgios V Gkoutos - One of the best experts on this subject based on the ideXlab platform.
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aber owl a framework for ontology based data access in biology
BMC Bioinformatics, 2015Co-Authors: Robert Hoehndorf, Luke T Slater, Paul N Schofield, Georgios V GkoutosAbstract:Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of Automated Reasoning.
Luke T Slater - One of the best experts on this subject based on the ideXlab platform.
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aber owl a framework for ontology based data access in biology
BMC Bioinformatics, 2015Co-Authors: Robert Hoehndorf, Luke T Slater, Paul N Schofield, Georgios V GkoutosAbstract:Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of Automated Reasoning.
John Mylopoulos - One of the best experts on this subject based on the ideXlab platform.
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Automated Reasoning for regulatory compliance
International Conference on Conceptual Modeling, 2013Co-Authors: Alberto Siena, Silvia Ingolfo, Anna Perini, Angelo Susi, John MylopoulosAbstract:Regulatory compliance is gaining attention from information systems engineers who must design systems that at the same time satisfy stakeholder requirements and comply with applicable laws. In our previous work, we have introduced a conceptual modelling language called Nomos 2 that aids requirements engineers analyze law to identify alternative ways for compliance. This paper presents an implemented Reasoning tool that supports analysis of law models. The technical contributions of the paper include the formalization of Reasoning mechanisms, their implementation in the NRTool, as well as an elaborated evaluation framework intended to determine whether the tool is scalable with respect to problem size, complexity as well as search space. The results of our experiments with the tool suggest that this conceptual modelling approach scales to real life regulatory compliance problems.
Robert Hoehndorf - One of the best experts on this subject based on the ideXlab platform.
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aber owl a framework for ontology based data access in biology
BMC Bioinformatics, 2015Co-Authors: Robert Hoehndorf, Luke T Slater, Paul N Schofield, Georgios V GkoutosAbstract:Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of Automated Reasoning.
Josef Urban - One of the best experts on this subject based on the ideXlab platform.
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property invariant embedding for Automated Reasoning
arXiv: Artificial Intelligence, 2019Co-Authors: Miroslav Olsak, Cezary Kaliszyk, Josef UrbanAbstract:Automated Reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an Automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.
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Learning-Assisted Automated Reasoning with Flyspeck
Journal of Automated Reasoning, 2014Co-Authors: Cezary Kaliszyk, Josef UrbanAbstract:The considerable mathematical knowledge encoded by the Flyspeck project is combined with external Automated theorem provers (ATPs) and machine-learning premise selection methods trained on the Flyspeck proofs, producing an AI system capable of proving a wide range of mathematical conjectures automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39 % of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.
