Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.

Fuzzy Inference Propelled Sentence Ranking for Extractive Summary Generation

Natural language serves as an impeccable tool for the appropriate representation of knowledge among individuals. Owing to the varying representation of the same knowledge base and the perpetual growth of the World Wide Web, the need to uncover an effective method to condense available textual data without significantly dampening the implied information is paramount. In an attempt to solve the need for effectively condensing textual data, the paper proposes a system which is capable of mimicking the human brain's approach to process Natural Language Fuzzy Logic. The system is subjected to both intrinsic and extrinsic evaluation and the results are compared against two other text summarizers - Auto summarize Tool and SweSum using the CNN Corpus Dataset. The Relevance Prediction Measure, F1 Score and Recall results suggest the applicability of Fuzzy Reasoning in text summarization and through evaluation, it can be inferred that proposed system has successfully tried to mimic the process of summary generation by the human brain.

From Action to Cognition: Neural Reuse, Network Theory and the Emergence of Higher Cognitive Functions

The aim of this article is to discuss the logic and assumptions behind the concept of neural reuse, to explore its biological advantages and to discuss the implications for the cognition of a brain that reuses existing circuits and resources. We first address the requirements that must be fulfilled for neural reuse to be a biologically plausible mechanism. Neural reuse theories generally take a developmental approach and model the brain as a dynamic system composed of highly flexible neural networks. They often argue against domain-specificity and for a distributed, embodied representation of knowledge, which sets them apart from modular theories of mental processes. We provide an example of reuse by proposing how a phylogenetically more modern mental capacity (mental rotation) may appear through the reuse and recombination of existing resources from an older capacity (motor planning). We conclude by putting arguments into context regarding functional modularity, embodied representation, and the current ontology of mental processes.

Knowledge-Based Recommendation for On-Demand Mapping: Application to Nautical Charts

Maps have long been seen as a single cartographic product for different uses, with the user having to adapt their interpretation to his or her own needs. On-demand mapping reverses this paradigm in that it is the map that adapts to the user’s needs and context of use. Still often manual and reserved for professionals, on-demand mapping is evolving toward an automation of its processes and a democratization of its use. An on-demand mapping service is a chain of several consecutive steps leading to a target map that precisely meets the needs and requirements of a user. This article addresses the issue of selecting relevant thematic layers with a specific context of use. We propose a knowledge-based recommendation approach that aims to guide a cartographer through the process of map-making. Our system is based on high- and low-level ontologies, the latter modeling the concepts specific to different types of maps targeted. By focusing on maritime maps, we address the representation of knowledge in this context of use, where recommendations rely on axiomatic and rule-based reasoning. For this purpose, we choose description logics as a formalism for knowledge representation in order to make cartographic knowledge machine readable.

La Audiodescripción desde la representación del conocimiento general. Configuración semántica de una gramática local del texto audiodescrito

Audio Description is a new text type which offers the prototypical receiver – blind or partially sighted people – a narrated representation of what is occurring at specific moments in the other audiovisual text to which it is subordinated. An AD script is subject to multiple subordination: to what is happening on the screen, to the distribution of silent spaces in the other text, and to the amount of time provided in each space. This fact, however, does not prevent the AD script from offering a representation of the general knowledge encoded by specific fragments of the film. Such a representation of knowledge, understood as the description in words or other signs of a specific reality, may serve as a semantic basis for the creation of a local grammar of AD scripts.

Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs.

Axiological density of the linguocultural concept

The article is devoted to determining the factors of axiological features of cognitive forms. The main approaches related to the evaluation specification of linguistic and cultural concepts (structural and semantic) are outlined and analyzed. It is explained that the structural features of the linguo-cultural concept (historical and actual layers, domains and modular parts, nuclear and near-nuclear zone) determine its axiological features. The structure of the representation of knowledge is evaluative specified by the characteristics of its name (internal form, in equivalence, denotation and connotation, etc.). It is argued that the axiological characteristics of the concept are determined by external factors: belonging to one or different cultures, a particular subculture, the amount of collective historical, subjective emotional, and concrete-sensory experience, position on other concepts, rooted in the system of background knowledge, carrier mentality, stereotypes, as well as subjects of material and spiritual culture, etc. The term “axiological density of linguistic and cultural concept” was introduced into scientific circulation and an algorithm for its definition was proposed with the help of an integrative approach. Based on this algorithm, the modal (axiological) component of the linguistic and cultural concept of “time” in the poetic discourse of the 20th-21st centuries is analyzed.

Outline of a Linguistic Model for the Study of Emerging Naming

The article presents the outline of a linguistic model that is part of a methodology for identifying and analyzing emerging or referentially unstable namings, such as cultural appropriation, street harassment, climate refugee or ecocide. The model and the method are intended to guide the interpretation – manual or semi-automatic – of the referential expressions, according to the semantic-cognitive type of the designated entity (human entity, social process, event, etc.), but also taking into account interdiscursive negotiations that affect the choice of terms and their uses. The proposed approach is original and is based on several guiding ideas: (1) take into account the complexity of the naming and the entanglement of his different facets which are categorization, meaning, performativity and valuation (desirability, preferences, social norms), (2) target the development phase of the naming (observe how speakers deal with the unstable): for this purpose, we will use the notion of identification between weak or identified entities and strong or reference entities, (3) report in an integrated way the referential elaboration of knowledge, the lexical and semantic elaboration of expressions, and the expression of intersubjective attitudes. The scientific framework combines three main disciplinary areas: automatic language processing (construction and representation of knowledge, reference), semantics (elaboration of meanings) and discourse analysis (interdiscursive elaboration of concepts and terms).