For example, an ontology can be: – a thesaurus in the field of information retrieval or – a model represented in OWL in the field of linked-data or Finally, we exemplify ontology engineering by summarizing our work. Types of ontology and epistemology . The ontology the team created covers a range of explanation types identified in the literature, and accounts for relationships between explanation types, the system interface, and user attributes. This is quite reasonable: given the potential variation in motivations as discussed in the previous section, it is unsurprising that different styles of ontology should develop. Ontology is the branch of philosophy that studies concepts such as existence, being, becoming, and reality.It includes the questions of how entities are grouped into basic categories and which of these entities exist on the most fundamental level. Ontology is traditionally listed as a part of the major branch of philosophy known as metaphysics. Artificial Intelligence (AI) is a 50+ year old academic discipline that provided many technologies that are now in commercial use. This paper concerns the role of ontology in Information Retrieval (IR) and Artificial Intelligence (AI). The ontology is the key to that understanding. Ontology and metaphysics both get confused with epistemology, but epistemology is easier to separate out. According to [7,8], the account of existence in this case is a pragmatic one: “For AI systems, what ‘exists’ is that which can be represented.” Computational ontologies are a means to formally model the structure The definition 1 is the meaning in philosophy as we have discussed above, however it has many implications for the AI purposes. The former deals with logic and knowledge representation and the latter content of knowledge. Knowledge plays an important role in demonstrating intelligent behavior in AI agents. AI cannot start from zero. One of the common ways to determine the scope of the ontology is to sketch a list of competency questions that a knowledge base based on the ontology … In artificial intelligence ( AI), an ontology is, according to Tom Gruber, an AI specialist at Stanford University, "the specification of conceptualizations, used to help programs and humans share knowledge." Explainable AI Explanation ontology Modeling of explanations and explanation types Supporting explainable ai in clinical decision making and decision support This is a preview of subscription content, log in to check access. III. Artificial intelligence (AI) is the field devoted to building artificial animals (or at least artificial creatures that – in suitable contexts – appear to be animals) and, for many, artificial persons (or at least artificial creatures that – in suitable contexts – appear to be persons). Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly used in critical, high-stakes settings such as healthcare. 1. Knowledge of real-worlds plays a vital role in intelligence and same for creating artificial intelligence. A conceptual overview of our explanation ontology, capturing attributes of explanations to allow them to be assembled by an AI Task, used in a system interacting with a user. According to Tom Gruber , a pioneer in AI exploration and semantic web technologies, AI researchers borrowed the term ontology from philosophy as an apt system for the ordering of knowledge systems that they required: Motivation The Evidence Ontology was developed to provide a controlled vocabulary of terms for defining the different types of experimental and computational evidence that support assertions within Pathway/Genome Databases such as the MetaCyc database . 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