Ontology (information science)
In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse.
Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.
What ontologies in both information science and philosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories.
However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles, or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes.
[9][10] While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius).
AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful.
In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems.
[20]As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity.
Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project.
Standardized upper ontologies available for use include BFO, BORO method, Dublin Core, GFO, Cyc, SUMO, UMBEL, and DOLCE.
[31][32] Ontology engineering aims to make explicit the knowledge contained in software applications, and organizational procedures for a particular domain.
Ontology engineering offers a direction for overcoming semantic obstacles, such as those related to the definitions of business terms and software classes.
Aspects of ontology editors include: visual navigation possibilities within the knowledge model, inference engines and information extraction; support for modules; the import and export of foreign knowledge representation languages for ontology matching; and the support of meta-ontologies such as OWL-S, Dublin Core, etc.
Information extraction and text mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges.
As epistemology is directly linked to knowledge and how we come about accepting certain truths, individuals conducting academic research must understand what allows them to begin theory building.
