Abstract: Knowledge graphs have recently emerged as a powerful way to represent knowledge in multiple communities, including data mining, natural language processing and machine learning. Large-scale knowledge graphs like Wikidata and DBpedia are openly available, while in industry, the Google Knowledge Graph is a good example of proprietary knowledge that continues to fuel impressive advances in Google's semantic search capabilities. Knowledge graphs are also intuitive, and it is possible to understand the basic concepts girding a knowledge graph without much technical background. This workshop will cover knowledge graphs from a broad perspective. Starting from plain English documents, we will construct simple knowledge graphs both by hand (at a small scale) and using NLP techniques like named entity recognition (at a larger scale). We will also look at the data to see why automatically constructing knowledge graphs is problematic, and consider practical techniques for 'refining' the knowledge graph further. We'll close the workshop by showing that, even when noisy, knowledge graphs can be useful in a data science pipeline, which makes them robust in deriving and representing knowledge from human-produced text.

Bio: Mayank Kejriwal is a research scientist and lecturer at the University of Southern California's Information Sciences Institute (ISI). He received his Ph.D. from the University of Texas at Austin. His dissertation involved Web-scale data linking, and in addition to being published as a book, was recently recognized with an international Best Dissertation award in his field. His research is highly applied and sits at the intersection of knowledge graphs, social networks, Web semantics, network science, data integration and AI for social good. He has contributed to systems that are being used by both DARPA and by law enforcement, and he has active collaborations in both academia and industry. He is currently co-authoring a textbook on knowledge graphs (MIT Press, 2018), and has delivered tutorials and demonstrations at numerous conferences and venues, including KDD, AAAI, and ISWC.

Open Data Science Conference