Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox [1].
Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques [2].
Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data [3, 4]. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
[1] http://bit.ly/15yrSemS [2] http://j.mp/SW-IFP [3] http://bit.ly/k-CCKS [4] https://arxiv.org/pdf/1908.06520.pdf
Founding Director, Artificial Intelligence Institute and Professor, Computer Science & Engineering
University of South Carolina
https://sc.edu/study/colleges_schools/engineering_and_computing/research/centersandinstitutes/ai_institute/index.php
Prof. Amit Sheth is an Educator, Researcher, and Entrepreneur. Prior to his joining the University of South Carolina as the founding director of the university-wide AI Institute, he was the LexisNexis Ohio Eminent Scholar and executive director of Ohio Center of Excellence in Knowledge-enabled Computing. He is a Fellow of IEEE, AAAI, and AAAS.
