Commonsense Knowledge, Ontology and Ordinary Language

TL;DR

This paper advocates for integrating ontological structures into logical semantics to better address complex natural language phenomena, bridging the gap between purely quantitative and knowledge-based NLP approaches.

Contribution

It introduces the idea of enriching logical semantics with ontological structures reflecting commonsense, addressing semantic challenges like metonymy and copredication.

Findings

Ontological structures help address semantic phenomena

Logical semantics can be enhanced with commonsense ontologies

Bridging quantitative and knowledge-based NLP approaches

Abstract

Over two decades ago a "quite revolution" overwhelmingly replaced knowledgebased approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learning) methods. Although it is our firm belief that purely quantitative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engineering approaches to the construction of large knowledge bases for NLP are somewhat justified. In this paper we hope to demonstrate that both trends are partly misguided and that the time has come to enrich logical semantics with an ontological structure that reflects our commonsense view of the world and the way we talk about in ordinary language. In this paper it will be demonstrated that assuming such an ontological structure a number of challenges in the semantics of natural language (e.g., metonymy, intensionality, copredication, nominal compounds, etc.) can be properly and uniformly addressed.