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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 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.

In this paper we want to investigate how to measure changes that occur when ontologies evolve in time. In the modern semantic online environments users cannot assume that initially created ontologies will remain static throughout the whole lifespan of particular application. Moreover, alignments originally established between such ontologies can become stale and invalid when certain changes have been applied to maintained ontologies. Therefore, such mappings should evolve in parallel. These issues raise some important questions: How to express changes introduced to ontologies? When the revalidation of alignments should occur? Is it necessary to relaunch an alignment procedure for the whole ontologies or is it possible to check and adjust only small fragments of mappings affected by applied modifications?