scholarly journals Semantic web technologies as enablers for truly connected mobility within smart cities

2019 ◽  
Vol 151 ◽  
pp. 31-36 ◽  
Author(s):  
Miloš Viktorović ◽  
Dujuan Yang ◽  
Bauke de Vries ◽  
Nico Baken
Keyword(s):  
Semantic Web ◽  
Smart Cities ◽  
Semantic Web Technologies ◽  
Web Technologies

scholarly journals Explainable Artificial Intelligence for Developing Smart Cities Solutions

Smart Cities ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1353-1382
Author(s):  
Dhavalkumar Thakker ◽  
Bhupesh Kumar Mishra ◽  
Amr Abdullatif ◽  
Suvodeep Mazumdar ◽  
Sydney Simpson
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Semantic Web ◽  
Image Classification ◽  
Smart Cities ◽  
Semantic Web Technologies ◽  
Hybrid Classifier ◽  
Web Technologies ◽  
Explainable Ai ◽  
Flood Monitoring

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

Cloud computing and semantic web technologies for ubiquitous management of smart cities-related competences

2020 ◽  
Author(s):  
Omiros Iatrellis ◽  
Theodor Panagiotakopoulos ◽  
Vassilis C. Gerogiannis ◽  
Panos Fitsilis ◽  
Achilles Kameas
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Smart Cities ◽  
Semantic Web Technologies ◽  
Web Technologies

scholarly journals Enabling Interoperability in the Internet of Things

2017 ◽  
Vol 13 (1) ◽  
pp. 147-167 ◽  
Author(s):  
Alfredo D'Elia ◽  
Fabio Viola ◽  
Luca Roffia ◽  
Paolo Azzoni ◽  
Tullio Salmon Cinotti
Keyword(s):  
Internet Of Things ◽  
Semantic Web ◽  
Smart Cities ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Public Authorities ◽  
Web Technologies ◽  
Data Store ◽  
Rdf Graphs ◽  
The Internet Of Things

Semantic Web technologies act as an interoperability glue among different formats, protocols and platforms, providing a uniform vision of heterogeneous devices and services in the Internet of Things (IoT). Semantic Web technologies can be applied to a broad range of application contexts (i.e., industrial automation, automotive, health care, defense, finance, smart cities) involving heterogeneous actors (i.e., end users, communities, public authorities, enterprises). Smart-M3 is a semantic publish-subscribe software architecture conceived to merge the Semantic Web and the IoT domains. It is based on a core component (SIB, Semantic Information Broker) where data is stored as RDF graphs, and software agents using SPARQL to update, retrieve and subscribe to changes in the data store. This article describes a OSGi SIB implementation extended with a new persistent SPARQL update primitive. The OSGi SIB performance has been evaluated and compared with the reference C implementation. Eventually, a first porting on Android is presented.

Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning

Applying Semantic Web Technologies to Drug Safety Determination

2006 ◽  
Vol 21 (1) ◽  
pp. 82-86 ◽  
Author(s):  
S. Stephens ◽  
A. Morales ◽  
M. Quinlan
Keyword(s):  
Semantic Web ◽  
Drug Safety ◽  
Semantic Web Technologies ◽  
Web Technologies
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