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Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review
Elvira Amador-Domínguez1, Emilio Serrano2, Daniel Manrique3
1Ontology Engineering Group, Department of Artificial Intelligence, ETSI Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain. eamador@fi.upm.es.
Sensors (Basel, Switzerland)
|April 25, 2019
Summary
Ambient Intelligence uses knowledge graphs to integrate diverse data for intelligent systems. This study reviews their use and predictive models, highlighting Knowledge Graph Embeddings for smart homes.
Area of Science:
- Artificial Intelligence
- Ambient Intelligence
- Knowledge Representation
Background:
- Ambient Intelligence is a growing field with many global initiatives.
- Knowledge bases and knowledge graphs are crucial for contextualizing and combining heterogeneous data.
- Ontologies provide higher-level constraints for intelligent systems.
Purpose of the Study:
- To systematically review knowledge base applications in intelligent environments.
- To study predictive and decision-making models used in Ambient Intelligence.
- To demonstrate the utility of Knowledge Graph Embeddings in smart home use cases.
Main Methods:
- Systematic literature review of knowledge bases in intelligent environments.
- In-depth analysis of predictive and decision-making models.
- Development and presentation of a smart home use case.
Main Results:
- Identified diverse applications of knowledge bases in intelligent environments.
- Analyzed various predictive and decision-making models.
- Demonstrated the practical advantages of Knowledge Graph Embeddings.
Conclusions:
- Knowledge bases are fundamental for creating context-aware information in Ambient Intelligence.
- Knowledge Graph Embeddings offer significant advantages for intelligent systems, particularly in smart home applications.
Keywords:
ambient intelligenceintelligent environmentknowledge baseknowledge graphknowledge graph embeddingreasoning modelMore Related Videos
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