It Is Time That the Classification of Entities Evolves

Abstract

Ontology, in general, is a shared, accepted, and comprehensive model (or a set of theories) of a particular application domain. One of the main benefits of adopting a domain ontology is the capacity to construct a semantic representation of the data along with the accompanying domain knowledge. New fields like bioinformatics and healthcare modeling, are using ontologies of various sizes and complexity to represent, analyze and communicate information (....). Convincing domain experts of the benefits and advantages of defining and using ontologies are critical barriers to widespread ontology adoption. Data modeling and information retrieval have recently incorporated semantic-based methods leveraging domain ontologies. By strengthening the interface between data and search requests, ontology-based information retrieval aims to better match the result sets to the users' performed search.

Furthermore, recent research of interviews with ontology professionals found that many of them routinely run the reasoner, sometimes after every revision, to find faults like unsatisfiable classes and stop the propagation of errors. Many ontologies are frequently present within one domain, and their contents may overlap. For data integration and ontology-based query and analytical tasks, mappings between such connected ontologies interrelate or link relevant and semantically related concepts. These mappings, for instance, allow the fusion of various related ontologies into a single, cohesive ontology.

While ontologies evolve at variable rates, no statistically significant variation in ontologies' rates of evolution could be found. Therefore, researchers can conclude that the impact of changes on materialization can be generalized to other ontologies. However, it seems that, aside from a few techniques, knowledge-driven query formulation methods that rely on OWL-assertion DL's capabilities, such as OWL-2, are still few.