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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.

Keywords:
ambient intelligenceintelligent environmentknowledge baseknowledge graphknowledge graph embeddingreasoning model

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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.