STIMONT: A core ontology for multimedia stimuli description
This work addresses retrieval inefficiencies for researchers in psychology and neuroscience using affective multimedia databases, though it is incremental as it builds on existing formats like EmotionML.
The paper tackles the problem of low recall and precision in retrieving affective multimedia stimuli from databases, which complicates emotion elicitation procedures, by introducing STIMONT, a core ontology that improves retrieval performance compared to keyword-based methods.
Affective multimedia documents such as images, sounds or videos elicit emotional responses in exposed human subjects. These stimuli are stored in affective multimedia databases and successfully used for a wide variety of research in psychology and neuroscience in areas related to attention and emotion processing. Although important all affective multimedia databases have numerous deficiencies which impair their applicability. These problems, which are brought forward in the paper, result in low recall and precision of multimedia stimuli retrieval which makes creating emotion elicitation procedures difficult and labor-intensive. To address these issues a new core ontology STIMONT is introduced. The STIMONT is written in OWL-DL formalism and extends W3C EmotionML format with an expressive and formal representation of affective concepts, high-level semantics, stimuli document metadata and the elicited physiology. The advantages of ontology in description of affective multimedia stimuli are demonstrated in a document retrieval experiment and compared against contemporary keyword-based querying methods. Also, a software tool Intelligent Stimulus Generator for retrieval of affective multimedia and construction of stimuli sequences is presented.