A Cloud-Based Time-Dependent Routing Service

Giannakopoulou, Kalliopi; Kontogiannis, Spyros; Papastavrou, Georgia; Zaroliagis, Christos

doi:10.1007/978-3-319-57045-7_4

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Except for 3cixty [19], which presents a tool for consulting a tourism and transport knowledge graph, the rest of the selected papers in this review use KBs for predictive purposes. Giannakopoulou et al [24], Olszewski and Turek [26] and Zavala et al [30] combine the KBs with data extracted from sensors and citizens, either from social media or by employing gamification techniques. Then, they combine this information for different purposes, namely predicting the best route available, diagnosing spatial problems on specific areas and context-modelling for smart vehicles, respectively.…”

Section: Ambient Intelligence Applications On Particular Domainsmentioning

confidence: 99%

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

Amador-Domínguez

Serrano

Paz

2019

Sensors

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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.

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