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.
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.
By ontology, we understand a knowledge structure which well reflects the complexity of a real world. Ontologies are built to store and process knowledge about objects and dependencies between them. Thus, ontologies not only structure raw data, but also contain the meaning of those data. So far, ontology developers have been forced to provide the semantics of modeled objects and relations between them manually. The goal of this paper is to address some still unresolved problems related to providing meanings in ontologies. A typical ontology consists of concepts with attributes, relations between them, and instances. In our research, we focus on concepts level where attributes must be interpretable because they are the primary carriers of the meaning of the entire ontology. In other words, we need to assign semantics for each attribute within a concept. For this purpose, we have proposed a semi-automatic method for defining attribute semantics based on WordNet. The attribute semantics designated by human experts and based on WordNet does not affect the integration result. However, the developed method allows reducing the time for preparing an ontology to the integration process. What is more, it is less sensitive to the subjective evaluations done by experts.
In recent years, the number of domain ontologies on the Internet has been steadily increasing. Many ontologies describe overlapping universes of discourse in various ways, therefore, the need for an efficient ontology alignment method is required. Currently, there are many solutions for this problem. However, the only known way to evaluate their output is to confront it with some pre-prepared reference alignment, therefore making it impossible to incorporate in real-world applications where no reference alignment is given. This paper presents some innovative methods of evaluating ontology alignments which allows assessing their quality without the aforementioned reference alignment. The main contribution are formal foundations of such methods, algorithms developed based on those foundations, and an experimental verification of their usefulness.
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