Search engines and other information systems have started to evolve from retrieving documents to providing more intelligent information access. However, the evolution is still in its infancy due to computers' limited ability in representing and understanding human language. This talk will present my work addressing these challenges with knowledge graphs. The first part is about utilizing entities from knowledge graphs to improve search. I will discuss how we build better text representations with entities and how the entity-based text representations improve text retrieval. The second part is about better text understanding through modeling entity salience (importance), as well as how the improved text understanding helps search under both feature-based and neural ranking settings. This talk concludes with future directions towards the next generation of intelligent information systems.
Chenyan Xiong is a Ph.D. candidate at Carnegie Mellon University. His research lies in the intersection of machine learning and information retrieval. His current research focus is on improving text representation and understanding in real-world information systems using knowledge graphs and neural networks. He is a recipient of Allen Institute for Artificial Intelligence research fellowship. Besides publishing papers, he also co-organizes NTCIR WWW Tracks about deep learning for search, the first SIGIR workshop on knowledge graphs and semantics for text retrieval and analysis, and a special issue in Information Retrieval Journal about knowledge graph for IR.
The AI Seminar is generously sponsored by Apple.