WOAH: Preliminaries to Zero-shot Ontology Learning for Conversational Agents

TL;DR

This paper introduces WOAH, a zero-shot method for estimating ontologies in conversational agents by extracting and analyzing linguistic dependencies to generate configurable, generalized ontologies.

Contribution

The paper proposes a novel zero-shot approach, WOAH, for ontology estimation that leverages dependency analysis and similarity metrics, advancing ontology learning for conversational agents.

Findings

Effective extraction of nouns and verbs from data.

Configurable levels of ontology generalization.

Potential for improved conversational agent development.

Abstract

The present paper presents the Weighted Ontology Approximation Heuristic (WOAH), a novel zero-shot approach to ontology estimation for conversational agents development environments. This methodology extracts verbs and nouns separately from data by distilling the dependencies obtained and applying similarity and sparsity metrics to generate an ontology estimation configurable in terms of the level of generalization.