Assessing Ontology Mappings on a Level of Concepts and Instances

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.

Integrating Heterogeneous Ontologies in Asian Languages Through Compact Genetic Algorithm with Annealing Re-sample Inheritance Mechanism

“…However, using the pre-prepared reference alignment making it impossible to incorporate in real-world applications where no reference alignment is given. Recently, Pietranik et al [27] present some approximate evaluating metrics which allows assessing the alignment's quality without the aforementioned reference alignment. Mohammadi et al [25] get the expert involved into the evaluation, and use the a multi-criteria decision-making method which combines multiple performance metrics with the expert's preferences.…”

Ontology-Based Methodology for Knowledge Acquisition from Groupware

“…There are also problems in finding the mappings between the harvested knowledge and the appropriate locations in the destination ontology or existing ontology since the concept is coming from sentences and has less information [16]. A few studies have indeed presented mapping procedures [17], matching [18][19][20], and alignment [21,22], but these studies are formulated based on combining two or more independent ontologies, and mostly using concept similarities that require a significant amount of computation and calculations. These are ontology-to-ontology specific, hence the literature remains scant on a technique for the identification and insertion (hooking) of new concepts from sentences into an existing ontology.…”