The Experts below are selected from a list of 625350 Experts worldwide ranked by ideXlab platform
C. Maria Keet - One of the best experts on this subject based on the ideXlab platform.
-
Evidence-based lean logic profiles for conceptual Data Modelling languages
arXiv: Artificial Intelligence, 2018Co-Authors: Pablo Rubén Fillottrani, C. Maria KeetAbstract:Multiple logic-based reconstruction of conceptual Data Modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three Modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable $\mathcal{ALNI}$). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual Data models.
Michael Zakharyaschev - One of the best experts on this subject based on the ideXlab platform.
-
A Cookbook for Temporal Conceptual Data Modelling with Description Logics
ACM Transactions on Computational Logic, 2014Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael ZakharyaschevAbstract:We design temporal description logics (TDLs) suitable for reasoning about temporal conceptual Data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z, og S pace and PS pace . These positive results are obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal Data models.
-
a cookbook for temporal conceptual Data Modelling with description logics
ACM Transactions on Computational Logic, 2014Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael ZakharyaschevAbstract:We design temporal description logics (TDLs) suitable for reasoning about temporal conceptual Data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,
-
a cookbook for temporal conceptual Data Modelling with description logics
arXiv: Logic in Computer Science, 2012Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael ZakharyaschevAbstract:We design temporal description logics suitable for reasoning about temporal conceptual Data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, Data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal Data models.
Pablo Rubén Fillottrani - One of the best experts on this subject based on the ideXlab platform.
-
Evidence-based lean logic profiles for conceptual Data Modelling languages
arXiv: Artificial Intelligence, 2018Co-Authors: Pablo Rubén Fillottrani, C. Maria KeetAbstract:Multiple logic-based reconstruction of conceptual Data Modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three Modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable $\mathcal{ALNI}$). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual Data models.
Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.
-
evolving spiking neural networks and neurogenetic systems for spatio and spectro temporal Data Modelling and pattern recognition
World Congress on Computational Intelligence, 2012Co-Authors: Nikola KasabovAbstract:Spatio- and spectro-temporal Data (SSTD) are the most common types of Data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such Data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal Data. Its organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems are potentially capable of Modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of Data in an adaptive and self-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG Data analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.
Pornpit Wongthongtham - One of the best experts on this subject based on the ideXlab platform.
-
differentiating conceptual Modelling from Data Modelling knowledge Modelling and ontology Modelling and a notation for ontology Modelling
Asia-Pacific Conference on Conceptual Modelling, 2008Co-Authors: Tharam S Dillon, Elizabeth Chang, Maja Hadzic, Pornpit WongthongthamAbstract:This paper considers conceptual Modelling for three purposes namely Data Modelling, knowledge Modelling and ontology Modelling. It differentiates between the nature of the conceptual models for these three. It then proposes a representation suitable for ontology Modelling.
