Tutorials: Knowledge Graphs: A Practical Introduction across Disciplines

Knowledge Graphs (KGs) like Wikidata, NELL and DBPedia have recently played instrumental roles in several applications of computational intelligence, including search and information retrieval, natural language processing, and data mining. The simplest definition of a KG is as a directed, labeled multi-network. Yet, despite being ubiquitous in the communities mentioned above, the full scope of KGs has not been explored across the computational intelligence community. With the rapid rise in Web data, there are many interesting and domain-specific opportunities in this area. We propose a tutorial that will provide a detailed and rigorous synthesis of KGs, along with discussing the application potential of KGs across multiple domains within computational intelligence.

Expected learning outcomes and target audience. This tutorial will be specifically designed for students, researchers and applied scientists (whether in academia or industry) in computational intelligence who have an interest in seeking further intersection and insights with research in knowledge graphs and knowledge discovery in the broader NLP, semantic web and data mining communities. Participants will only be expected to have a very basic knowledge of machine learning.