Using MathML to Represent Units of Measurement for Improved Ontology Alignment

TL;DR

This paper introduces a method using MathML to enhance ontology alignment by accurately representing units of measurement, leading to improved matching across different ontologies.

Contribution

The paper proposes a novel approach that employs MathML for better semantic representation of units, facilitating more accurate ontology alignment.

Findings

MathML-based representation improves alignment accuracy

Mapping results for three ontologies demonstrate effectiveness

Approach outperforms traditional lexical comparison methods

Abstract

Ontologies provide a formal description of concepts and their relationships in a knowledge domain. The goal of ontology alignment is to identify semantically matching concepts and relationships across independently developed ontologies that purport to describe the same knowledge. In order to handle the widest possible class of ontologies, many alignment algorithms rely on terminological and structural meth- ods, but the often fuzzy nature of concepts complicates the matching process. However, one area that should provide clear matching solutions due to its mathematical nature, is units of measurement. Several on- tologies for units of measurement are available, but there has been no attempt to align them, notwithstanding the obvious importance for tech- nical interoperability. We propose a general strategy to map these (and similar) ontologies by introducing MathML to accurately capture the semantic description of concepts specified therein. We provide mapping results for three ontologies, and show that our approach improves on lexical comparisons.