Ontology Alignment in the Cloud

Information Sciences, 2012

theseus.joint-research.org

International Journal of Intelligent Systems, 2012

Advances in Intelligent Systems and Computing, 2016

2015

With the semantic web, data becomes machine-readable and ontologies define the data. Ontologies in any domain are heterogeneous due to rapid increase in ontology development and differences in views of developers. Agents can fully understand the data only if the correspondences between ontologies are known. Various ontology alignment systems have been developed to automatically discover such correspondences. However, human involvement is still indispensible because the results provided by fully automatic systems are not always complete or precise. This paper introduces Falcon-AO++, an extension of the Falcon-AO alignment system that supports the interactive contribution of a domain expert in the matching process. The evaluation results have shown that contribution of an expert and matching ability of matchers can improve alignment results.

This work is a research in the field of Ontologies Integration from the point of view of Ontology Mining based on Services. Specifically, the work focuses on an automatic suggestion of ontological alignments for users. The Ontology Mining area (OM) is very recent, due to the current trend of using ontologies as a mechanism for representing knowledge, which has created a wide field to explore and extract knowledge. The problem lies in the comparison of existing ontologies, in order to use them together, that is, finding their semantic equivalences. There are different techniques for ontologies alignment as a form of ontological comparison based on the matching of the concepts, which is a fundamental process in the ontologies integration. Each alignment technique uses different strategies; based on specific principles, which make it more adequate for a particular context. This paper proposes an automatic approach for comparison and selection of alignment techniques, given a group of ontologies, based on the ABC algorithm, which is inspire by bee colonies. The approach uses as comparison and selection criteria the execution time, the number of aligned concepts, and the number of times the colony chooses each technique (this is due to the stochastic approach of the ABC algorithm)

2010

ABSTRACT Ontology alignment is recognized as a fundamental process to achieve an adequate interoperability between people or systems that use different, overlapping ontologies to represent common knowledge. This process consists of finding the semantic relations between different ontologies. There are different techniques conceived to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results.

Discrete Dynamics in Nature and Society, 2020

Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a...

Advances in computational intelligence and robotics book series, 2015