Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Design of formal languages and interfaces: "formal" does not mean "unreadable".

This chapter provides an introduction to a work that aims to apply the achievements of engineering psychology to the area of formal methods, focusing on the specification phase of a system development process. Formal methods often assume that only two factors should be satisfied: the method must be sound and give such a representation, which is concise and beautiful from the mathematical point of view, without taking into account any question of readability, usability, or tool support. This leads to the fact that formal methods are treated by most engineers as something that is theoretically important but practically too hard to understand and to use, where even some small changes of a formal method can make it more understandable and usable for an average engineer

Ranking Semantic Web Services Using Rules Evaluation and Constraint Programming

Current Semantic Web Services discovery and ranking proposals are based on user preferences descriptions whose expressiveness are limited by the underlying logical formalism used. Thus, highly expressive preference descriptions, such as utility functions, cannot be handled by the kind of reasoners traditionally used to perform Semantic Web Services tasks. in this work, we outline a hybrid approach to allow the introduction of utility functions in user preferences descriptions, where both rules evaluation and constraint programming are used to perform the ranking process. Our proposal extends the Web Service Modeling Ontology with these descriptions, providing a highly expressive framework to specify preferences, and enabling a more general ranking process, which can be performed by different engines

A Service Ranker Based on Logic Rules Evaluation and Constraint Programming

Ranking of Semantic Web Services is usually performed based on user preferences descriptions. These descriptions are expressed in terms of an underlying logical formalism, which limits their expressiveness. Thus, there are some kind of descriptions, such as utility functions, that cannot be handled by reasoners currently being used to perform Semantic Web Services tasks, though utility functions provide a higher level of expressiveness. in this work, we present a hybrid solution to allow the introduction of utility functions in user preferences descriptions, using both Logic Programming rules evaluation and Constraint Programming to perform the ranking process. This proposal is based on the Web Service Modeling Ontology, extending it with a highly expressive framework to specify user preferences, and enabling the integration of different engines to perform the ranking process

The Role of Consciousness in Memory

Conscious events interact with memory systems in learning, rehearsal and retrieval (Ebbinghaus 1885/1964; Tulving 1985). Here we present hypotheses that arise from the IDA computional model (Franklin, Kelemen and McCauley 1998; Franklin 2001b) of global workspace theory (Baars 1988, 2002). Our primary tool for this exploration is a flexible cognitive cycle employed by the IDA computational model and hypothesized to be a basic element of human cognitive processing. Since cognitive cycles are hypothesized to occur five to ten times a second and include interaction between conscious contents and several of the memory systems, they provide the means for an exceptionally fine-grained analysis of various cognitive tasks. We apply this tool to the small effect size of subliminal learning compared to supraliminal learning, to process dissociation, to implicit learning, to recognition vs. recall, and to the availability heuristic in recall. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. In most cases, memory is hypothesized to interact with conscious events for its normal functioning. The methodology of the paper is unusual in that the hypotheses and explanations presented are derived from an empirically based, but broad and qualitative computational model of human cognition

Semantic medical care in smart cities

Medical care is a vitally important part of successful smart cities further development. High quality medical treatment has always been a challenging task for administrative departments of cities government. The key reason is that the treatment of patients significantly depends on the skills of medical stuff that can hardly be controlled and estimated. Semantic technologies by now have showed capabilities to solve highly complicated badly formalized problems in conditions of uncertainty. It makes reasonable to apply them in medical domain. In the paper a real example of information system for semantic medical care is presented. The system is being developed for Federal Almazov North-West Medical Research Centre in St-Petersburg, Russia (http://www.almazovcentre.ru/?lang=en). The main attention is paid to the proposed solution for the problem of medical treatment estimation in administrative and managerial departments. We focus on medical treatment examinations matching, trend analysis and administrative analytical and prediction task solving making use of semantic technologies, statistical analysis and deep learning applied to huge amounts of diverse data. Semantic medical data analysis project is an attempt to proceed to semantic medicine - an interoperable approach to medical domain area

A dynamic network Aapproach to the study of syntax

Usage-based linguists and psychologists have produced a large body of empirical results suggesting that linguistic structure is derived from language use. However, while researchers agree that these results characterize grammar as an emergent phenomenon, there is no consensus among usage-based scholars as to how the various results can be explained and integrated into an explicit theory or model. Building on network theory, the current paper outlines a structured network approach to the study of grammar in which the core concepts of syntax are analyzed by a set of relations that specify associations between different aspects of a speaker’s linguistic knowledge. These associations are shaped by domain-general processes that can give rise to new structures and meanings in language acquisition and language change. Combining research from linguistics and psychology, the paper proposes specific network analyses for the following phenomena: argument structure, word classes, constituent structure, constructions and construction families, and grammatical categories such as voice, case and number. The article builds on data and analyses presented in Diessel (2019 ; The Grammar Network. How Linguistic Structure is Shaped by Language Use ) but approaches the topic from a different perspective

Ontologies for Industry 4.0

The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.Peer ReviewedPostprint (published version