Explanation Ontology: A Model of Explanations for User-Centered AI

no code implementations • 4 Oct 2020 • Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das, Deborah L. McGuinness

With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities.

Explanation Ontology in Action: A Clinical Use-Case

no code implementations • 4 Oct 2020 • Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das, Deborah L. McGuinness

We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl. org/heals/eo).

Making Study Populations Visible through Knowledge Graphs

no code implementations • 9 Jul 2019 • Shruthi Chari, Miao Qi, Nkcheniyere N. Agu, Oshani Seneviratne, James P. McCusker, Kristin P. Bennett, Amar K. Das, Deborah L. McGuinness

To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations.

Knowledge Graphs