Building Domain-Specific Knowledge with Human in the Loop
Speaker: Yunyao Li (IBM Research)
Date and Time: 12pm-1pm CT, September 11, Friday
The ability to build large-scale domain-specific knowledge bases that capture and extend the implicit knowledge of human experts is the foundation for many AI systems. We use an ontology-driven approach for the creation, representation and consumption of such domain-specific knowledge bases. This approach relies on several well-known building blocks: natural language processing, entity resolution, data transformation and fusion. We will present several human-in-the-loop work that target domain experts (rather than programmers) to extract the domain knowledge from the human expert and map it into the "right" models or algorithms. We will also share successful use cases in several domains, including Compliance, Finance, and Healthcare: by using these tools we can match the level of accuracy achieved by manual effort, but at a significantly lower cost and much higher scale and automation. If time permits, we will demonstrate a knowledge base built for the Finance domain.
Yunyao Li is a Principal Research Staff Member and Senior Research Manager at IBM Almaden Research Center where she manages the Scalable Knowledge Intelligence department. She is a member of the inaugural New Voices program of the American National Academies. She is also a Master Inventor and a member of IBM Academy of Technology. Her expertise is in the interdisciplinary areas of natural language processing, databases, human-computer interaction, and information retrieval. She is a founding member of SystemT, a state-of-the-art NLP system currently powering 20+ IBM products, and numerous research projects. She received her PhD and master degrees from the University of Michigan, Ann Arborand undergraduate degrees from Tsinghua University, Beijing, China.