Tagging multimedia stimuli with ontologies

TL;DR

This paper proposes using ontologies to improve the description and retrieval of emotionally annotated multimedia stimuli, enhancing precision and flexibility in affective computing applications.

Contribution

It introduces ontologies as a novel paradigm for describing emotionally annotated data, enabling more efficient semantic content extraction and data management.

Findings

Ontologies improve semantic description of emotional data.

Enhanced data retrieval efficiency in affective computing.

Facilitates reuse in semantic web and social semantic desktop.

Abstract

Successful management of emotional stimuli is a pivotal issue concerning Affective Computing (AC) and the related research. As a subfield of Artificial Intelligence, AC is concerned not only with the design of computer systems and the accompanying hardware that can recognize, interpret, and process human emotions, but also with the development of systems that can trigger human emotional response in an ordered and controlled manner. This requires the maximum attainable precision and efficiency in the extraction of data from emotionally annotated databases While these databases do use keywords or tags for description of the semantic content, they do not provide either the necessary flexibility or leverage needed to efficiently extract the pertinent emotional content. Therefore, to this extent we propose an introduction of ontologies as a new paradigm for description of emotionally annotated data. The ability to select and sequence data based on their semantic attributes is vital for any study involving metadata, semantics and ontological sorting like the Semantic Web or the Social Semantic Desktop, and the approach described in the paper facilitates reuse in these areas as well.