Next Article in Journal
New Approach to Analysis of Selected Measurement and Monitoring Systems Solutions in Ship Technology
Previous Article in Journal
Mobile Robot Indoor Positioning Based on a Combination of Visual and Inertial Sensors
Article Menu
Issue 8 (April-2) cover image

Export Article

Open AccessArticle

Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review

1
Ontology Engineering Group, Department of Artificial Intelligence, ETSI Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain
2
Artificial Intelligence Lab, Department of Artificial Intelligence, ETSI Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain
3
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37007 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1774; https://doi.org/10.3390/s19081774
Received: 28 February 2019 / Revised: 5 April 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
  |  
PDF [552 KB, uploaded 13 April 2019]
  |  

Abstract

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context. View Full-Text
Keywords: knowledge base; knowledge graph; intelligent environment; ambient intelligence; reasoning model; knowledge graph embedding knowledge base; knowledge graph; intelligent environment; ambient intelligence; reasoning model; knowledge graph embedding
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Amador-Domínguez, E.; Serrano, E.; Manrique, D.; De Paz, J.F. Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review. Sensors 2019, 19, 1774.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top