Using Semantic Web and Linked Data for Integrating and Publishing Data in Smart Cities

Abid, Tarek; Laouar, Mohamed Ridda

doi:10.1145/3330089.3330132

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Earlier literature have developed application-specific ontologies and semanticsbased systems. Abid et al [31] repurposed existing ontologies to develop a defect detection system capable of publishing user complaints of city problems such as water leaks and broken street lights as linked data to aid in the administration of smart cities. Synapse [32] is a semantic web-based annotation system designed to enhance the metadata for all data in smart cities.…”

Section: Related Workmentioning

confidence: 99%

A semantic web approach to uplift decentralized household energy data

Orlandi

AlSkaif

et al. 2022

Sustainable Energy, Grids and Networks

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Section: Related Workmentioning

confidence: 99%

A semantic web approach to uplift decentralized household energy data

Orlandi

AlSkaif

et al. 2022

Sustainable Energy, Grids and Networks

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“…With the extremely complex nature of IoT data for smart cities such as the variety of representations, distribution, scales and densities, the semantic web offers a solution by providing a layer of abstraction, standardisation and semantics to solve interoperability issues [73]. Furthermore, this facilitates the fusion of a variety of IoT data with existing knowledge from standard data sources and linked data [74,75]. One of the challenges of smart cities has been integrating a large number of applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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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.

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