Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

Abstract

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

Ontological engineering for the definition of a COVID-19 pandemic ontology

“…Ontological Engineering refers to the activities linked to the ontology development process, including the methodologies, tools, and languages required for building ontologies [ 16 , 33 , 34 , 36 , 37 ]. One of the current main domains is Ontological Mining, which consists of extracting behavior patterns, knowledge, and other characteristics, using mining techniques to build or enrich ontologies [ 16 ].…”

“…It is also called fusing or mixing. It is a process where several ontologies within the same knowledge domain come together to standardize knowledge, make knowledge grow, or have locally complete knowledge [ 36 , 37 ]. Mixing is required when ontologies handle the same domain but with different or partial representations, such that the ontologies can coincide in certain concepts and not in others.…”

“…The basis ontologies are extracted from the literature and are highly specialized to one domain (e.g., IDO). Also, the relationships between them are specified, following the integration strategy defined by the ontological engineering process [ 16 , 33 , 34 , 36 , 37 ]. Thus, the first step is the alignment between them, and according to their domains, the integration can be a merge/mixture or a linking between them.…”

“…Thus, the first step is the alignment between them, and according to their domains, the integration can be a merge/mixture or a linking between them. When the ontologies are in the same knowledge domain, then they must be merged/fused; and when they are complementary, then they must be linked [ 16 , 36 , 37 ].…”

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

“…The semantic similarity between the concepts or nodes can be calculated using different methods. These methods have been categorized into four ways, namely path-based similarity measure, information content similarity measure, feature-based similarity measure and hybrid similarity measure (Varelas et al, 2005;Andrews and Hirsch, 2016;Rangel et al, 2018). Table III represents the subcategories of each method along with its principle, merits and demerits and some of the existing ontology merging methods based on the similarity measure.…”