Knowledge Graph Dataset for Semantic Enrichment of Picture Description in NAPS Database

Abstract

This data description introduces a comprehensive knowledge graph (KG) dataset with detailed information about the relevant high-level semantics of visual stimuli used to induce emotional states stored in the Nencki Affective Picture System (NAPS) repository. The dataset contains 6808 systematically manually assigned annotations for 1356 NAPS pictures in 5 categories, linked to WordNet synsets and Suggested Upper Merged Ontology (SUMO) concepts presented in a tabular format. Both knowledge databases provide an extensive and supervised taxonomy glossary suitable for describing picture semantics. The annotation glossary consists of 935 WordNet and 513 SUMO entities. A description of the dataset and the specific processes used to collect, process, review, and publish the dataset as open data are also provided. This dataset is unique in that it captures complex objects, scenes, actions, and the overall context of emotional stimuli with knowledge taxonomies at a high level of quality. It provides a valuable resource for a variety of projects investigating emotion, attention, and related phenomena. In addition, researchers can use this dataset to explore the relationship between emotions and high-level semantics or to develop data-retrieval tools to generate personalized stimuli sequences. The dataset is freely available in common formats (Excel and CSV).

Keywords

• picture stimuli
• affective pictures databases
• image tagging
• knowledge representation
• affective computing