MULTI-AGENT LATENT SEMANTIC INTERNET TECHNOLOGY FOR THE FORMATION OF A SUBJECT-ORIENTED KNOWLEDGE MODEL

Context. The article proposes a latent-semantic technology for extracting information from Internet resources, which allows processing information in natural language, as well as a multi-agent search algorithm based on it. The relevance of this approach to the search for subject-oriented information determined by the fact that currently a direct lexical comparison of queries with document indexes does not fully satisfy the developer. The object of the study is a multi-agent latent-semantic algorithm for searching for subject-oriented information. Objective. The work is to increase the efficiency of forming a knowledge model that is adequate for this subject area. Method. A latent semantic technology based on the weighted descriptor method developed by the authors is proposed. The main difference from the existing methods is that the analysis of words occurring in the text both in frequency and taking into account semantics carried out by selecting the appropriate descriptors, which improves the quality of the information found. Results. The developed latent-semantic technology of information search tested in the task of constructing a knowledge model of automated decision support systems for operational and dispatching control of urban engineering networks. The conducted modeling of the search for subject-oriented information in this subject area showed the effectiveness of the developed approach. Conclusions. Improving the efficiency of search and semantic content of subject-oriented information of the knowledge model of this subject area achieved by using the weighted descriptor method based on Zipf’s laws in this technology. The prospects for further research are to build evolutionary models of knowledge and improve the quality of updated information.

Consent through the lens of semantics: State of the art survey and best practices

The acceptance of the GDPR legislation in 2018 started a new technological shift towards achieving transparency. GDPR put focus on the concept of informed consent applicable for data processing, which led to an increase of the responsibilities regarding data sharing for both end users and companies. This paper presents a literature survey of existing solutions that use semantic technology for implementing consent. The main focus is on ontologies, how they are used for consent representation and for consent management in combination with other technologies such as blockchain. We also focus on visualisation solutions aimed at improving individuals’ consent comprehension. Finally, based on the overviewed state of the art we propose best practices for consent implementation.

Using Semantic Technology To Model Persona For Adaptable Agents

In state of the art research a growing interest in the application of agent models for the simulation of road traffic can be observed. Software agents are particularly suitable for the representation of travellers and their goal-oriented behaviour. Although numerous applications based on these types of models are already available, the options for modelling and calibration of the agents as goal-oriented individuals are either simplified to aggregated parameters or associated with overly complex and opaque implementation details. This makes it difficult to reuse available simulation models. In this paper, we demonstrate how the combination of persona models together with semantic methods can be applied to achieve a well-structured agent model that allows for improved reusability.

Research Data Infrastructures and Engineering Metadata

This chapter introduces metadata models as a semantic technology for knowledge representation to describe selected aspects of a research asset. The process of building a hierarchical metadata model is reenacted in this chapter and highlighted by the example of EngMeta. Moreover, an overview on data infrastructures is given, the general architecture and functions are disscussed, and multiple examples of data infrastructures in materials modelling are given.

Semantic Technology for Simulations and Molecular Particle-Based Methods

In this Chapter we discuss the role of ontologies for simulations, in the context of materials modelling in general and of molecular particle-based methods in particular. After a brief overview of the literature and possible applications, we present the VIMMP ontologies that allow to describe software capabilities and to further specify the various algorithms via the involved variables: the VImmp Ontology of Software (VISO) and the Vimmp Ontology of Variables (VOV).

FoFA: Diet Information for Children with Autism with Semantic Technology in Android Based Application

The number of people with autism in Indonesia increases by 0.15% or 6,900 children per year. One of the actions that can be done to overcome developmental disorders of children with autism is to do Feingold and Failsafe Diet, Specific Carbohydrate Diet (SCD diet), and Casein-Free Gluten Free diet (CFGF diet) on foodstuffs given to children with autism. There is a need for socialization and presentation of information regarding the regulation of food items given to children with autism. Currently, there is no presentation of information in the form of mobile-based applications as a forum for parents to exchange information, especially those that utilize semantic technology. By utilizing semantic technology, the Food For Autism (FoFA) application was created to share knowledge for users related to food and beverage diet menus for children with autism. The test results show that the application of FoFA can apply semantic technology related to diet and food diets for children with autism.