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In information science, ontology engineering is the study of methodologies used to build representations of knowledge within a specified domain and to clarify the relationship between those concepts. An ontology has two purposes: to describe a domain and to provide conceptualization to reason about the entities within that domain. Therefore, an ontology is a formal concept, both explicit and shared. Ontologies provide the structure framework for a variety of informational organization, applied in such fields as software engineering, life sciences, biomedical informatics, artificial intelligence and information architecture. Engineering of ontologies is thus often defined as a set of interrelated tasks that develop the ontology for a specific domain.
Defining vocabulary is an important attribute of ontology engineering. Seeking common terms, defining the level of formality for various terms, specifying their meaning, and defining the relationship between the terms and formality levels are central to the process. Focusing on the development and improvement of this process, the ontology life cycle, the methodologies used, and the tools and languages that support the process are considered the interrelated tasks of ontology engineering. Due to the widespread use of ontologies across a wide range of domains, ontology engineering has become an important process marked with progressive refinement.
Also known as ontology building, ontology engineering is thus a sub-field of knowledge engineering and is best described as the study of methods used to construct ontologies. Making explicit the knowledge conceptualized in software applications, within enterprises and business processes across a specified domain is thus the aim of ontology engineering. Solving semantic obstacles of inoperability is considered one of the vital directions of the field. An example is addressing obstacles presented when naming business processes and assigning those processes to relevant software classes.
Distinct advantages are offered by deploying refined ontology engineering to build accurate ontologies. New ontologies can be built from existing components of already established ontologies. Multiple resources and applications as well can share the ontologies, providing for expediency. Biomedical science ontologies for example, can borrow from life sciences and vice-versa, thus saving time, money and resources. Constructing knowledge databases from scratch usually has the opposite impact.
As such, ontology engineering offers the potential to transform the knowledge industry. Rather than domains focusing on building knowledge databases from scratch when building new knowledge-based systems, they instead can borrow specialized terminology when dealing with related concepts. Therefore, this allows systems engineers to shift focus to sharing and dissemination methodologies, while also relegating appropriate resource toward building more powerful hardware to house, access and process these ontologies.
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