Document Type
Article
Subject Area(s)
Artificial Intelligence
Abstract
During the last decade, traditional data-driven deep learning (DL) has shown remarkable success in essential natural language processing tasks, such as relation extraction. Yet, challenges remain in developing artificial intelligence (AI) methods in real-world cases that require explainability through human interpretable and traceable outcomes. The scarcity of labeled data for downstream supervised tasks and entangled embeddings produced as an outcome of self-supervised pre-training objectives also hinders interpretability and explainability. Additionally, data labeling in multiple unstructured domains, particularly healthcare and education, is computationally expensive as it requires a pool of human expertise. Consider Education Technology, where AI systems fall along a “capability spectrum” depending on how extensively they exploit various resources, such as academic content, granularity in student engagement, academic domain experts, and knowledge bases to identify concepts that would help achieve knowledge mastery for student goals. Likewise, the task of assessing human health using online conversations raises challenges for current statistical DL methods through evolving cultural and context-specific discussions. Hence, developing strategies that merge AI with stratified knowledge to identify concepts that would delineate healthcare conversation patterns and help healthcare professionals decide. Such technological innovations are imperative as they provide consistency and explainability in outcomes. This tutorial discusses the notion of explainability and interpretability through the use of knowledge graphs in (1) Healthcare on the Web, (2) Education Technology. This tutorial will provide details of knowledge-infused learning algorithms and its contribution to explainability for the above two applications that can be applied to any other domain using knowledge graphs.
Publication Info
Preprint version ACM CoDS-COMAD Conference, 2020.
© Association for Computing Machinery, 2020.
APA Citation
Gaur, M., Desai, A., Faldu, Keyur, & Sheth, A. (2020). Explainable AI using knowledge graphs. ACM CoDS-COMAD Conference.
Included in
Computer Engineering Commons, Educational Methods Commons, Electrical and Computer Engineering Commons, Public Health Commons