Now showing items 21-40 of 729

    • Understanding Clinician Workflows to Design AI Risk Prediction Models 

      Zhang, O.; Chari, Shruthi; Saiz, F. S.; Gruen, Daniel; Acharya, P.; Seneviratne, Oshani; Meter, P.; McGuinness, Deborah L.; Chakraborty, P. (AMIA, 2022-04)
    • Knowledge graphs: Introduction, history, and perspectives 

      Chaudhri, V. K.; Baru, C.; Chittar, N.; Dong, X. L.; Genesereth, M.; Hendler, James A.; Kalyanpur, A.; Lenat, D.; Sequeda, Juan; Vrandečić, D.; Wang, K. (Wiley, 2022-03-31)
      Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central ...
    • Auto-Transfer: Learning to Route Transferrable Representations 

      Murugesan, Keerthiram; Sadashivaiah, Vijay; Luss, Ronny; Shanmugam, Karthikeyan; Chen, Pin-Yu; Dhurandhar, Amit (International Conference on Learning Representations, 2022-03-15)
      Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications. Existing ...
    • A Theoretically Grounded Benchmark for Evaluating Machine Commonsense 

      Santos, Henrique; Shen, Ke; Mulvehill, Alice M.; Razeghi, Yasaman; McGuinness, Deborah L.; Kejriwal, Mayank (arXiv, 2022-03)
      Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (and in relatively fewer cases, generative) ...
    • End-to-End Table Question Answering via Retrieval-Augmented Generation 

      Pan, FeiFei; Canim, Mustafa; Glass, Michael; Gliozzo, Alfio; Hendler, James A. (arXiv, 2022-03)
      Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader to locate the correct answers from table ...
    • InDO: the Institute Demographic Ontology 

      Keshan, Neha; Fontaine, Kathy; Hendler, James A. (Springer, Cham, 2022-01-01)
      Graduate education institutes in the United States (US) have been working on programs to increase the number of students and faculty from marginalized communities. When choosing to pursue a doctoral degree, the common ...
    • Incentivized Research Data Sharing, Reusing, and Repurposing with Blockchain Technologies 

      Seneviratne, Oshani; McGuinness, Deborah L. (University of Hawaii at Manoa, 2022)
      Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data ...
    • A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research 

      Bennett, Kristin P.; Erickson, John S.; Svirsky, Amy; Seddon, Josephine (The Mathematics Enthusiast, 2021-12-16)
      This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real- world problems for industry, university, ...
    • Developing the Cross-Disciplinary Information Model for NASA’s Science Mission Directorate 

      Duerr, Ruth; Eleish, Ahmed; Parsons, Mark; Berrios, Daniel; Bugbee, Kaylin; Fox, Peter (AGU, 2021-12)
      The five divisions of NASAs Science Mission Directorate (SMD) represent a very broad spectrum of academic disciplines, ranging from Astronomy, to Planetary science, to Heliophysics, Earth science, Biology and Physical ...
    • InDO: the Institute Demographic Ontology 

      Keshan, Neha; Fontaine, Kathy; Hendler, James A. (Springer, Cham, 2021-11-22)
      Graduate education institutes in the United States (US) have been working on programs to increase the number of students and faculty from marginalized communities. When choosing to pursue a doctoral degree, the common ...
    • Towards Clinically Relevant Explanations for Type-2 Diabetes Risk Prediction with the Explanation Ontology 

      Chari, Shruthi; Chakraborty, Prithwish; Seneviratne, Oshani; Ghalwash, Mohamed; Gruen, Daniel M.; Sow, Daby; McGuinness, Deborah L. (AMIA, 2021-11)
    • Enhancing Clinical Relevance of Health Behavior Insights via Semantics 

      Harris, Jonathan; McGuinness, Deborah L.; Monti, Marco; Seneviratne, Oshani; Zaki, Mohammed J.; Chen, Ching-Hua (AMIA, 2021-11)
    • Geospatial Reasoning with shapefiles for Supporting Policy Decisions 

      Santos, Henrique; McCusker, Jamie; McGuinness, Deborah L. (2021-10-12)
      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 their attributes satisfy certain criteria. ...
    • Dimensions of Commonsense Knowledge 

      Ilievski, Filip; Oltramari, Alessandro; Ma, Kaixin; Zhang, Bin; McGuinness, Deborah L.; Szekely, Pedro (Knowledge-Based Systems, 2021-10-11)
      Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed ...
    • Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations 

      Seneviratne, Oshani; Harris, Jonathan; Chen, Ching-Hua; McGuinness, Deborah L. (AKBC, 2021-10)
      We propose a knowledge model for capturing dietary preferences and personal context to provide personalized dietary recommendations. We develop a knowledge model called the Personal Health Ontology, which is grounded in ...
    • The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features 

      Patton, E.W.; Van Woensel, W.; Seneviratne, Oshani; Loseto, G.; Scioscia, F.; Kagal, L. (Springer, Cham, 2021-10)
      Modern smartphones offer advanced sensing, connectivity, and processing capabilities for data acquisition, processing, and generation: but it can be difficult and costly to develop mobile research apps that leverage these ...
    • BlockIoT: Blockchain-based Health Data Integration using IoT Devices 

      Shukla, Manan; Lin, Jianjing; Seneviratne, Oshani (arXiv, 2021-10)
      The development and adoption of Electronic Health Records (EHR) and health monitoring Internet of Things (IoT) Devices have enabled digitization of patient records and has also substantially transformed the healthcare ...
    • AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-Ray 

      Agu, Nkechinyere; Wu, Joy T.; Chao, Hanqing; Lourentzou, Ismini; Sharma, Arjun; Moradi, Mehdi; Yan, Pingkun; Hendler, James A. (Springer-Verlag, Berlin, Heidelberg, 2021-09-27)
      Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look at the entire X-ray image for classification, ...
    • The Problem of Fairness in Synthetic Healthcare Data 

      Bhanot, Karan; Qi, Miao; Erickson, John S.; Guyon, Isabelle; Bennett, Kristin P. (Entropy (Basel, Switzerland), 2021-09)
      Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private ...
    • Knowledge Graphs 

      Hogan, Aidan; Gutierrez, Claudio; Cochcz, Michael; de Melo, Gerard; Kirranc, Sabrina; Pollcrcs, Axel; Navigli, Roberto; Ngonga Ngomo, Axcl-Cyrille; Rashid, Sabbir; Schmclzciscn, Lukas; Staab, Stefan; Blomqvist, Eva; d’Amato, Claudia; Gayo, José Emilio Labra; Ncumaicr, Sebastian (Springer, Cham, 2021-09)
      This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying ...