The HaMSE Ontology: Using Semantic Technologies to support Music Representation Interoperability and Musicological Analysis

TL;DR

The paper introduces HaMSE, an ontology leveraging Semantic Web technologies to enhance music representation interoperability and support detailed musicological analysis across various data types.

Contribution

It presents a novel ontology, HaMSE, that bridges different music representation systems and integrates quantitative and qualitative data for musicological research.

Findings

Enables alignment of diverse music representation systems

Supports analysis at multiple granularity levels

Facilitates integration of quantitative and qualitative musical data

Abstract

The use of Semantic Technologies - in particular the Semantic Web - has revealed to be a great tool for describing the cultural heritage domain and artistic practices. However, the panorama of ontologies for musicological applications seems to be limited and restricted to specific applications. In this research, we propose HaMSE, an ontology capable of describing musical features that can assist musicological research. More specifically, HaMSE proposes to address sues that have been affecting musicological research for decades: the representation of music and the relationship between quantitative and qualitative data. To do this, HaMSE allows the alignment between different music representation systems and describes a set of musicological features that can allow the music analysis at different granularity levels.