Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining
Lap Poon Rupert Tang, Raymond J. Mooney · Texas ScholarWorks (Texas Digital Library) · 2003
portant information for disambiguation.A bottom-up approach is more suitable for learning the former, while top-down is better for the latter.By allowing both approaches to induce program clauses and choosing the best combination of their results, Cocktail learns more effective parsers.Experimental results on learning natural-language interfaces for two databases demonstrate that it learns more accurate parsers than Chillin, the previous best method for this task.Beth is a new integrated ILP algorithm for relational data mining.The Inverse Entailment approach to ILP, implemented in the Progol and Aleph systems, starts with the construction of a bottom clause, the most specific hypothesis covering a seed example.When mining relational data with a large number of background facts, the bottom clause becomes intractably large, making learning very inefficient.A top-down approach heuristically guides the construction of clauses without building a bottom clause; however, it wastes time exploring clauses that cover no positive examples.By using a top-down approach to heuristically guide the construction of generalizations of a bottom clause, Beth combines the strength of both approaches.Learning patterns for detecting potential terrorist activity is a current challenge problem for relational data mining.Experimental results on artificial data for this task with over half a million facts show that Beth is significantly more efficient at discovering such patterns than Aleph and m-Foil, two leading ILP systems.