Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs

TL;DR

This paper extends the WordNet-DOLCE mapping to include verbs by linking them to noun-based perdurants, enhancing semantic annotation accuracy in NLP tasks by 9.05%.

Contribution

It introduces a novel method for aligning verbs with foundational ontology classes based on their noun counterparts, expanding ontology-based annotation.

Findings

Achieved a 9.05% increase in annotation accuracy.

Extended WordNet-DOLCE mapping to verbs.

Improved semantic annotation framework performance.

Abstract

Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks. A previous alignment between WordNet noun synsets and DOLCE provided a starting point for ontology-based annotation, but in NLP tasks verbs are also of substantial importance. This work presents an extension to the WordNet-DOLCE noun mapping, aligning verbs according to their links to nouns denoting perdurants, transferring to the verb the DOLCE class assigned to the noun that best represents that verb's occurrence. To evaluate the usefulness of this resource, we implemented a foundational ontology-based semantic annotation framework, that assigns a high-level foundational category to each word or phrase in a text, and compared it to a similar annotation tool, obtaining an increase of 9.05% in accuracy.