Abductive Equivalential Translation and its application to Natural Language Database Interfacing

TL;DR

This paper formalizes natural-language database interfacing using abductive equivalential translation, enabling logical inference to relate linguistic forms to database primitives, demonstrated through implementation in a real database system.

Contribution

It introduces Abductive Equivalential Translation (AET) as a formal inference framework for natural-language database interfaces, with reductions to goal-directed inference and practical implementation.

Findings

AET can handle various interface functionalities like answering questions and commands.

The approach simplifies solving the 'Doctor on Board' problem.

Implementation in a real database demonstrates practical viability.

Abstract

The thesis describes a logical formalization of natural-language database interfacing. We assume the existence of a ``natural language engine'' capable of mediating between surface linguistic string and their representations as ``literal'' logical forms: the focus of interest will be the question of relating ``literal'' logical forms to representations in terms of primitives meaningful to the underlying database engine. We begin by describing the nature of the problem, and show how a variety of interface functionalities can be considered as instances of a type of formal inference task which we call ``Abductive Equivalential Translation'' (AET); functionalities which can be reduced to this form include answering questions, responding to commands, reasoning about the completeness of answers, answering meta-questions of type ``Do you know...'', and generating assertions and questions. In each case, a ``linguistic domain theory'' (LDT) $\Gamma$ and an input formula $F$ are given, and the goal is to construct a formula with certain properties which is equivalent to $F$, given $\Gamma$ and a set of permitted assumptions. If the LDT is of a certain specified type, whose formulas are either conditional equivalences or Horn-clauses, we show that the AET problem can be reduced to a goal-directed inference method. We present an abstract description of this method, and sketch its realization in Prolog. The relationship between AET and several problems previously discussed in the literature is discussed. In particular, we show how AET can provide a simple and elegant solution to the so-called ``Doctor on Board'' problem, and in effect allows a ``relativization'' of the Closed World Assumption. The ideas in the thesis have all been implemented concretely within the SRI CLARE project, using a real projects and payments database. The LDT for the example database is described in detail, and examples of the types of functionality that can be achieved within the example domain are presented.