Ewetasker
Ewetasker is an emotion aware automation platform developed by GSI group based on semantic ECA (Event-Condition-Action) rules. It is capable of enable semantic automation rules in a smart environment allowing the user to configure his own automation rules in an easy way. For this reason, plugins have been developed semantically as channels to make possible integrations with services as Twitter or Github and with devices as smart lights, beacons or smartphones.
This demo shows some of the capabilities of Ewetasker.
EWETASKER Channels
Ewetasker channels allow users to model semantically services and devices, specifying their events and actions in order to create rules that perform the automations. They have been developed in Notation3 following Ewe ontology specialized designed to describe elements within Task Automation Services enabling rule interoperability. For knowing more about Ewetasker, please visit Ewetasker documentation.
ONTOLOGIES
Emospaces is based on three specific ontologies in order to achieve the goal of adapt smart environments to users' emotions:
ONTOLOGY |
DESCRIPTION |
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Evented WEb Ontology (EWE) is designed to describe elements within Task Automation Services enabling rule interoperability. For knowing more about EWE, please visit the ontology specification. |
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Onyx is designed to annotate and describe the emotions expressed by user-generated content on the web or in particular Information Systems. For knowing more about Onyx, please visit the ontology specification. |
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Pearl is designed to describe opinions from the Semantic Web and linking them with contextual information (such as opinion topic, features described in the opinion etc.). For knowing more about Pearl, please visit the ontology specification. |
Publications
- Prototype of a Sentiment Analysis System Based on Ensemble Algorithms for Combining Deep and Surface Machine Learning Techniques, Oscar Araque. (2016). Prototype of a Sentiment Analysis System Based on Ensemble Algorithms for Combining Deep and Surface Machine Learning Techniques. Trabajo Fin de Titulación. ETSI Telecomunicación, Universidad Politécnica de Madrid
- Development of an Emotion Aware Ambient Intelligent System for Smart Offices. Application in a Living Lab and in a Social Simulated Scenario,Sergio Muñoz López. (2017). Development of an Emotion Aware Ambient Intelligent System for Smart Offices. Application in a Living Lab and in a Social Simulated Scenario. Trabajo Fin de Titulación (TFM). ETSI Telecomunicación, Universidad Politécnica de Madrid
- Design and Implementation of a Google Action enabled Smart Agent System for Mobile App Review Monitoring based on Sentiment Analysis Techniques, Antonio Fernández. (2017). Design and Implementation of a Google Action enabled Smart Agent System for Mobile App Review Monitoring based on Sentiment Analysis Techniques. Trabajo Fin de Titulación (PFC). ETSI Telecomunicación, Universidad Politécnica de Madrid
- Sematch: Semantic Similarity Framework for Knowledge Graphs, Ganggao Zhu & Carlos A. Iglesias. (2017). Sematch: Semantic Similarity Framework for Knowledge Graphs. Knowledge-Based Systems
- Enhancing Deep Learning Sentiment Analysis with Ensemble Techniques in Social Applications, Oscar Araque, Ignacio Corcuera-Platas, J. Fernando Sánchez-Rada & Carlos A. Iglesias. (2017). Enhancing Deep Learning Sentiment Analysis with Ensemble Techniques in Social Applications. Expert Systems with Applications
- Semantic Similarity Analysis and Application in Knowledge Graphs, Ganggao Zhu. (2017). Semantic Similarity Analysis and Application in Knowledge Graphs. Tesis doctoral. ETSI Telecomunicación, Universidad Politécnica de Madrid
- Applying Recurrent Neural Networks to Sentiment Analysis of Spanish Tweets, Oscar Araque, Rodrigo Barbado, J. Fernando Sánchez-Rada & Carlos A. Iglesias (2017). Applying Recurrent Neural Networks to Sentiment Analysis of Spanish Tweets. In Ceur WS (editor)
- Soil: An Agent-Based Social Simulator in Python for Modelling and Simulation of Social Networks, Jesús M. Sánchez, Carlos A. Iglesias & J. Fernando Sánchez-Rada (2017). Soil: An Agent-Based Social Simulator in Python for Modelling and Simulation of Social Networks, chapter Advances i, pages 234-245. Springer Verlag
- Modeling Social Influence in Social Networks with SOIL, a Python Agent-Based Social Simulator, Eduardo Merino, Jesús M. Sánchez, David García Martín, J. Fernando Sánchez-Rada & Carlos A. Iglesias (2017). Modeling Social Influence in Social Networks with SOIL, a Python Agent-Based Social Simulator, chapter Advances i, pages 337-341. Springer-Verlag
- Neural Domain Adaptation of Sentiment Lexicons, Oscar Araque, Marco Guerini, Carlo Strapparava & Carlos A. Iglesias (2017). Neural Domain Adaptation of Sentiment Lexicons. In Proceedings of ACII 2017. San Antonio, Texas, USA.
- An Emotion Aware Task Automation Architecture Based on Semantic Technologies for Smart Offices, Sergio Muñoz López, Oscar Araque, J. Fernando Sánchez-Rada & Carlos A. Iglesias. (2018). An Emotion Aware Task Automation Architecture Based on Semantic Technologies for Smart Offices. Sensors, 18 (5), 1499