# Hyperintensional Reasoning based on Natural Language Knowledge Base

## TL;DR

This paper introduces a hyperintensional reasoning system based on Tichy's Transparent Intensional Logic, enabling more precise natural language analysis by recognizing different context types and avoiding over- or under-inference.

## Contribution

It presents a novel hyperintensional reasoning framework using TIL with context recognition, advancing natural language understanding beyond traditional intensional logic.

## Key findings

- Developed a context recognition algorithm for three context types
- Implemented an inference machine for hyperintensional logic
- Achieved more accurate reasoning in natural language analysis

## Abstract

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy's Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.