Enterprises are often at a loss to determine whether they should choose a classification system based on ontology or a classification system based on taxonomy. The hesitation arises from the fact that ontology is frequently confused with taxonomy and vice versa. Therefore, it is important for enterprises to identify the critical distinctions between ontology and taxonomy.
Both taxonomy and ontology belong to the fields of artificial intelligence (AI), the Semantic Web and system engineering. However, the difference lies in the depth of classification offered by them. For instance, let us take taxonomy classifications such as occupational information network (O*NET) and European skills/competencies, qualifications and occupations (ESCO). They offer a simpler approach to classifying objects by leveraging a hierarchical structure and using only parent-child relations without any additional, more sophisticated links.
Further, in the classification of ESCO, it can be observed that all medical specialists are grouped under the heading ‘Specialist Medical Practitioners’. Additionally, specialist skill sets are simply grouped in lists without any links to the respective specialist occupations. This methodology is followed because, in taxonomies, specializations can only be recognized by their job title. Consequently, to understand their individual meaning one needs to refer to other sources.
In contrast, ontology of occupations, qualifications, and skills can facilitate automatic recognition of the similarities and differences existing between job titles. For example, pediatricians and neonatologists have similar jobs. However, with the ontology modeling approach, it is possible to determine a pediatrician and neonatologist posses a similar set of skills. Albeit the ontology clearly represents that pediatricians can only take over the neonatologist’s job after further training. This delineation is possible because information is represented in ontology through the interrelationships between concepts, which is beyond the capacity of a simple taxonomy.
There are other advantages to using ontologies. While matching datasets such as curriculum vitae (CV) and a job vacancy, it is important to compare the semantics (the underlying meaning) of two items rather than the wording. This is where ontologies come into play. They can provide semantic modeling that can detect the underlying meanings and similarities in CVs and job descriptions.
From the example given above, it is obvious that both taxonomy and ontology can help enterprises access and organize information effectively. However, the key lies in understanding the goal of the classification initiative and employing the appropriate model.
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