Abstract Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements.

The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.

Ontology alignment, although widely researched and discussed, has still some issues that need addressing. One of those is designating alignments of relations defined in two independent ontologies. According to the found literature, this topic is frequently omitted by many state-of-the-art ontology alignment solutions, that focus mainly on aligning concepts. In this paper, we propose a novel approach to designating mappings of ontology relations, which is based on fuzzy logic. It is used to combine several different similarity measures calculated between elements that are used to define relations. The approach has been experimentally verified using the widely accepted datasets provided by Ontology Alignment Evaluation Initiative, yielding promising results.

The retailers are interested in different profit maximization approaches, which help them to perform daily shelf arrangement tasks, improve customers’ satisfaction and avoid out of stocks on the shelves. Most of the retail literature proposes basic models which don’t reflect complicated merchandising rules. The aim of the paper is to develop a shelf space allocation model which investigates vertical shelf levels, horizontal shelf division into segments, and product item allocation rules such as cappings and nestings. A genetic algorithm has been developed to implement the model. The efficiency was estimated with the help of CPLEX solver. The computational experiments show that the proposed approach allows getting the results of sufficient quality for different problem sizes in a short running time without requiring large computing resources. GA and CPLEX found statistically the same profitable solution within the same computational time. However, GA found a better solution in situations where the commercial solver can’t perform calculations during the increased computational time and stopped after a couple of minutes. This proves the reason for GA implementation for shelf space allocation problems.

One of the most common applications of neural networks itself is data prediction models, for example, future stock market prices, calculated based on historical data. Spiking neural networks are one of the emerging architectures showing great potential in solving complex problems in complicated information environments. However, to the best of our knowledge, the spiking neural networks have not been successfully applied in stock market data prediction. The values of exchange-traded funds (ETF), due to their flexibility and simplicity, can be a good application of such a tool. Therefore, the following article provides the results of a comprehensive experimental comparison of different spiking neural networks in predicting ETF values. The main goal was to check if the spiking neural networks obtain better or worse results of forecasting than traditional neural networks. The secondary goal was a comparison of different spiking neural network architectures between themselves to judge which one is the most applicable to the given problem

The production of food in an ecologically and economically sustainable manner is of significant importance today. Agricultural producers are increasingly being accompanied by elements of Agriculture 4.0 such as automation and decision-making support. This work shows an example of how the digitization of viticulture can be significantly supported by Deep Learning. The work presents an approach that can overcome the loss of human expertise in grape identification by using image-recognition-techniques and residual network architectures. Our developed model for grape identification at a vineyard reaches an accuracy of 99% of correctly recognized grape varieties.

The following paper investigates a multilevel approach to data integration using the widely accepted Consensus Theory. We focus on an issue related to an initial classification of raw input data into groups that can be integrated in parallel. A final consensus is a result of the integration of obtained partial outcomes. Our main research concerns an application of Fleiss' kappa value, which in the literature is a well known measure that describes how consonant the data in a selected set are. In other words - for a given set of values, the higher the value of this measure, the higher its inner consistency. Therefore, we have attempted to answer the question whether or not the initial data should be divided into coherent groups or into highly divergent subsets, that better represent the whole input. We present a theoretical background, broad description of a series of experiments that we have performed and their statistical analysis.

The aim of this work is to give the definition and present the possibility of applying (introduced and defined here) ontotriggers to design the ontology of a maturity capsule used in the assessment of IT projects. The complexity of designing ontology processes raises the question of whether there is a need for designing ontologies in a situation where it is possible to map them. The work is divided into four main parts. The first part presents and defines the concept of an ontotrigger. The second part presents a model maturity capsule. Similarities to the maturity capsule of a project managed in accordance with the SCRUM methodology have also been indicated. The third part discusses the method of building ontologies for both capsules and indicates the possibility of mapping them. The fourth part presents the application of an ontotrigger which uses the ability to map both ontologies. In summary, the applicability of ontotriggers has been demonstrated for the design of ontologies of any class and their objects. The process of verifying this applicability for two maturity capsules: the model and SCRUM maturity capsules, showed that the design of ontologies for any IT project management method can be implemented through maturity capsule ontotriggers rather than designing new ontologies.

In this article, a method for ontology alignment, which is based on using semantics of attributes describing concepts, is presented. This work is an extended version of our previous works on ontology alignment, which is the task of designating correspondences between two ontologies. After investigating recent approaches, we noticed a lack of analysis of the lowest level of expressing knowledge within them. We treat attributes of concepts as this level and we claim that embedding their precise definition, semantics, and the ways in which they can interact with each other in the process of mapping ontologies can enhance former solutions of this problem. We show that developing such an approach brings us closer to a consistent and more intuitive methodology of aligning ontologies.