Learning to parse database queries using inductive logic programming

TLDR

The study introduces CHILL, a system that automates building natural‑language interfaces for database queries. CHILL learns shift‑reduce parser control rules via inductive logic programming from a corpus of sentence–query pairs, inducing parsers that translate natural language into executable database queries. Experiments on a U.S.

Abstract

This paper presents recent work using the CHILL parser acquisition system to automate the construction of a natural-language interface for database queries. CHILL treats parser acquisition as the learning of search-control rules within a logic program representing a shift-reduce parser and uses techniques from Inductive Logic Programming to learn relational control knowledge. Starting with a general framework for constructing a suitable logical form, CHILL is able to train on a corpus comprising sentences paired with database queries and induce parsers that map subsequent sentences directly into executable queries. Experimental results with a complete database-query application for U.S. geography show that CHILL is able to learn parsers that outperform a preexisting, hand-crafted counterpart. These results demonstrate the ability of a corpus-based system to produce more than purely syntactic representations. They also provide direct evidence of the utility of an empirical approach at the level of a complete natural language application.