The RatioLog Project: Rational Extensions of Logical Reasoning

TL;DR

The RatioLog project enhances deep question answering by integrating information retrieval, automated deduction, defeasible reasoning, and machine learning to improve logical reasoning and handle incomplete or inconsistent knowledge.

Contribution

It introduces a multi-phase approach combining retrieval, theorem proving, defeasible reasoning, and learning techniques for rational question answering.

Findings

Improved answer verification using logical representations.

Integration of defeasible reasoning for answer comparison.

Enhanced accuracy through machine learning on semantic structures.

Abstract

Higher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia "Wikipedia". In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase - because the knowledge may be incomplete and inconsistent -, we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.