27 citations · 134 across the 20 of their papers we have counts for
23 papers
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
Shruthi Chari, Prasant Acharya, Daniel M. Gruen +8
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inference…
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
Henrique Santos, Ke Shen, Alice M. Mulvehill +3
Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (an…
Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations
Oshani Seneviratne, Jonathan Harris, Ching-Hua Chen +1
We propose a knowledge model for capturing dietary preferences and personal context to provide personalized dietary recommendations. We develop a knowledge model called the Persona…
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
Shruthi Chari, Prithwish Chakraborty, Mohamed Ghalwash +7
Academic advances of AI models in high-precision domains, like healthcare, need to be made explainable in order to enhance real-world adoption. Our past studies and ongoing interac…
Geospatial Reasoning with Shapefiles for Supporting Policy Decisions
Henrique Santos, James P. McCusker, Deborah L. McGuinness
Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and th…
Semantic Modeling for Food Recommendation Explanations
Ishita Padhiar, Oshani Seneviratne, Shruthi Chari +2
With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainabi…
Applying Personal Knowledge Graphs to Health
Sola Shirai, Oshani Seneviratne, Deborah L. McGuinness
Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems. In…
Commonsense Knowledge Mining from Term Definitions
Zhicheng Liang, Deborah L. McGuinness
Commonsense knowledge has proven to be beneficial to a variety of application areas, including question answering and natural language understanding. Previous work explored collect…
Dimensions of Commonsense Knowledge
Filip Ilievski, Alessandro Oltramari, Kaixin Ma +3
Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that includ…
Exploring and Analyzing Machine Commonsense Benchmarks
Henrique Santos, Minor Gordon, Zhicheng Liang +2
Commonsense question-answering (QA) tasks, in the form of benchmarks, are constantly being introduced for challenging and comparing commonsense QA systems. The benchmarks provide q…
A Semantic Framework for Enabling Radio Spectrum Policy Management and Evaluation
H. Santos, A. Mulvehill, J. S. Erickson +11
Because radio spectrum is a finite resource, its usage and sharing is regulated by government agencies. These agencies define policies to manage spectrum allocation and assignment…
Explanation Ontology: A Model of Explanations for User-Centered AI
Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen +3
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…
Explanation Ontology in Action: A Clinical Use-Case
Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen +3
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).…
Directions for Explainable Knowledge-Enabled Systems
Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne +1
Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex,…
Foundations of Explainable Knowledge-Enabled Systems
Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne +1
Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these…
Making Study Populations Visible through Knowledge Graphs
Shruthi Chari, Miao Qi, Nkcheniyere N. Agu +5
Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that…
Semantically-aware population health risk analyses
Alexander New, Sabbir M. Rashid, John S. Erickson +2
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Exampl…
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
Yue Liu, Tongtao Zhang, Zhicheng Liang +2
We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural ma…
Knowledge Integration for Disease Characterization: A Breast Cancer Example
Oshani Seneviratne, Sabbir M. Rashid, Shruthi Chari +4
With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been ch…
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
Yue Liu, Tao Ge, Kusum S. Mathews +2
In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because m…
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
Henrique Santos, Victor Dantas, Vasco Furtado +2
In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values h…
Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
Paulo Pinheiro, Deborah L. McGuinness, Henrique Santos
Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies th…
Contextual Data Collection for Smart Cities
Henrique Santos, Vasco Furtado, Paulo Pinheiro +1
As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under…