Building Industry-specific Knowledge Bases

Building industry-specific knowledge bases relies heavily on collecting and representing domain knowledge over time. Domain knowledge includes: (1) the logical schema, constraints and domain vocabulary of the application, (2) the models and algorithms to populate instances of that schema, and (3) the data necessary to build and maintain those models and algorithms. In IBM Watson we are using an ontology-driven approach for the creation and consumption of industry-specific knowledge bases. The creation of such knowledge bases involves well known building blocks: natural language processing, entity resolution, data transformation, etc. It is critical that the models and algorithms that implement these building blocks be transparent and optimizable for efficient execution. In this talk, I will describe the design of domain-specific languages (DSL) with specialized constructs that serve as target languages for learning these models and algorithms, and the generation of training data for scaling up the learning.