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 \( {{\mathcal{S}\mathcal{R}\mathcal{O}\mathcal{I}\mathcal{Q}}^{{({\mathcal{D}})}}} \) 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.
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Notes
- 1.
In the \( {{\mathcal{S}\mathcal{R}\mathcal{O}\mathcal{I}\mathcal{Q}}} \) description logic. Many less expressive DLs do not provide inverse roles, and no other ontology supports the universal role, which has been introduced in \( {{\mathcal{S}\mathcal{R}\mathcal{O}\mathcal{I}\mathcal{Q}}} \).
- 2.
Semantic Web Rule Language.
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Sikos, L.F. (2016). A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_1
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