Abstract
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.
Highlights
In recent years, the number of domain ontologies has been steadily increasing
It is caused by the fact that they provide a convenient and expressive way of describing some universe of discourse. Their foundation is a set of well-defined concepts, which represent classes of objects from the real world, along with relationships that occur between them
When communication of two independently developed information systems that utilize ontologies is expected, some kind of a bridge between them is obviously required. This task can be described as designating which elements of ontologies express the same parts of a modeled universe of discourse to eventually create a set of mappings between ontologies
Summary
The number of domain ontologies has been steadily increasing. It is caused by the fact that they provide a convenient and expressive way of describing some universe of discourse. Maintaining the current mappings is an important task of ontology developers [15] and none of the classical measures presented provides an easy way for continuously assessing the correctness and the quality of alignments. Designating a mapping between two ontologies involves finding their common parts The result of this process is a set of corresponding elements from compared ontologies, connected by some relationship at the some confidence level. Some of the systems described in the literature, such as SORAL (Supervised Ontology Relation ALignment), attempt to efficiently map relations, but the results of these research are not publicly available [9] For this reason, in this paper we focused only on the ontology alignment on the level of concepts and instances. Note that both e1 and e2 must refer to elements from the same level, there is no possibility that a correspondence between a concept and an instance exists
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