Models and vocabularies
EmotionML is a W3C recommendation to represent emotion related states in data processing systems. Use cases for EmotionML can be grouped into three broad types:
- Manual annotation of material involving emotionality, such as annotation of videos, of speech recordings, of faces, of texts, etc;
- Automatic recognition of emotions from sensors, including physiological sensors, speech recordings, facial expressions, etc., as well as from multi-modal combinations of sensors;
- Generation of emotion-related system responses, which may involve reasoning about the emotional implications of events, emotional prosody in synthetic speech, facial expressions and gestures of embodied agents or robots, the choice of music and colors of lighting in a room, etc.
Lemon is a proposed model for modelling lexicon and machine-readable dictionaries and linked to the Semantic Web and the Linked Data cloud. It was designed to meet the following challenges
- RDF-native form to enable leverage of existing Semantic Web technologies (SPARQL, OWL, RIF etc.).
- Linguistically sound structure based on LMF to enable conversion to existing offline formats.
- Separation of the lexicon and ontology layers, to ensure compatibility with existing OWL models.
- Linking to data categories, in order to allow for arbitrarily complex linguistic description. In particular the LexInfo vocabulary is aligned to lemon and ISOcat.
- A small model using the principle of least power - the less expressive the language, the more reusable the data.
Lemon was developed by the Monnet project as a collaboration between: CITEC at Bielefeld University, DERI at the National University of Ireland, Galway, Universidad Politécnica de Madrid and the Deutsche Forschungszentrum für Künstliche Intelligenz
Marl is a standardised data schema (also referred as "ontology" or "vocabulary") designed to annotate and describe subjective opinions expressed on the web or in particular Information Systems. The following document contains the description of the ontology and instructions on how to connect it with descriptions of other resources.
Onyx aims to complement the Marl Ontology by providing a simple means to describe emotion analysis processes and results using semantic technologies.