Structural Knowledge Discovery in Chemical and Spatio-Temporal Databases

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Student Abstracts

Most current knowledge discovery systems use only attribute-value information. But relational information between objects is also important to the knowledge hidden in today’s databases. Two such domains are chemical structures and domains where objects are related in space and time. Inductive Logic Programming (ILP) discovery systems handle relational data, but require data to be expressed as a subset of first-order logic. We are investigating the application of the graph-based relational discovery system Subdue in structural domains.