A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets

TL;DR

This paper introduces VidOnt, a highly expressive multimedia ontology designed for advanced reasoning over audiovisual datasets, addressing limitations of previous ontologies by incorporating complex role definitions and logical constructors.

Contribution

It presents VidOnt, the first multimedia ontology with SROIQ(D) expressivity and DL-safe rules, validated with industry-leading reasoners, and offers best practices for multimedia ontology engineering.

Findings

VidOnt enables more comprehensive multimedia reasoning.

Validated with HermiT and FaCT++ reasoners.

Provides best practices for ontology development.

Abstract

Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach.