Ontology:Emotional Knowledge Graph (EmoKG)
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Ontology Overview
| Name: | Emotional Knowledge Graph |
|---|---|
| Description: | EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). |
| Purpose: | Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health. Amelie Gyrard and Karima Boudaoud. MDPI Applied Sciences 2022. Special Issue Affective Computing and Recommender Systems. |
| Organization(s): | M3 (Machine-to-Machine Measurement), SWoT (Semantic Web of Things). http://sensormeasurement.appspot.com/ |
| Author(s): | Amelie Gyrard and Karima Boudaoud. |
| Justification | Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health. Amelie Gyrard and Karima Boudaoud. MDPI Applied Sciences 2022. Special Issue Affective Computing and Recommender Systems. |
| Recommended by: | |
| Submitted by: | AmelieGyrard |
| Competency Questions: | CQ: What are the basic emotions according to Eckman et al. [81]?
CQ: What are the neurotransmitters relevant for emotions? CQ: What are the hormones relevant for emotions? CQ: What are the hormones related to stress? CQ: What are the hormones related to happiness? CQ: What are the physiological parameters/sensors relevant to deduce (basic) emotions? CQ: How to deduce meaningful information such as (basic) emotions from physiological data produced by sensors? CQ: What are the ontologies describing emotions? |
| Domains: | Emotion, Internet of Things, Internet of Things (IoT) |
| Scenario: | Emotion, Naturopathy |
| Known issues: | |
| OntologyURI: | |
| Licensing: | M3 is under GNU GPLv3 license. |
| Web references: | |
| Other references: |
Long Description
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health. Amelie Gyrard and Karima Boudaoud. MDPI Applied Sciences 2022. Special Issue Affective Computing and Recommender Systems.
Additional Information
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