Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering

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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Towards knowledge-based geovisualisation using Semantic Web technologies: a knowledge representation approach coupling ontologies and rules

International Journal of Digital Earth ◽

10.1080/17538947.2019.1604835 ◽

2019 ◽

Vol 13 (9) ◽

pp. 976-997 ◽

Cited By ~ 4

Author(s):

Weiming Huang ◽

Lars Harrie

Keyword(s):

Semantic Web ◽

Knowledge Representation ◽

Semantic Web Technologies ◽

Web Technologies ◽

Knowledge Based

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Leveraging Semantic Web Technologies for Consistency Management in Multi-viewpoint Systems Engineering

Semantic Web Technologies for Intelligent Engineering Applications ◽

10.1007/978-3-319-41490-4_13 ◽

2016 ◽

pp. 327-352 ◽

Cited By ~ 4

Author(s):

Simon Steyskal ◽

Manuel Wimmer

Keyword(s):

Semantic Web ◽

Systems Engineering ◽

Semantic Web Technologies ◽

Consistency Management ◽

Web Technologies

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A Survey of Semantic Web Based Architectures for Adaptive Intelligent Tutoring System

Multidisciplinary Computational Intelligence Techniques ◽

10.4018/978-1-4666-1830-5.ch015 ◽

2012 ◽

pp. 239-256

Author(s):

Suraiya Jabin ◽

K. Mustafa

Keyword(s):

Artificial Intelligence ◽

Semantic Web ◽

Intelligent Tutoring ◽

Main Idea ◽

Semantic Web Technologies ◽

Web Based ◽

Tutoring Systems ◽

Web Technologies ◽

Artificial Intelligence In Education ◽

Web Based Education

Most recently, IT-enabled education has become a very important branch of educational technology. Education is becoming more dynamic, networked, and increasingly electronic. Today’s is a world of Internet social networks, blogs, digital audio and video content, et cetera. A few clear advantages of Web-based education are classroom independence and availability of authoring tools for developing Web-based courseware, cheap and efficient storage and distribution of course materials, hyperlinks to suggested readings, and digital libraries. However, there are several challenges in improving Web-based education, such as providing for more adaptivity and intelligence. The main idea is to incorporate Semantic Web technologies and resources to the design of artificial intelligence in education (AIED) systems aiming to update their architectures to provide more adaptability, robustness, and richer learning environments. The construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This chapter addresses state of the art Semantic Web methods and tools used for modeling and designing intelligent tutoring systems (ITS). Also it draws attention of Semantic Web users towards e-learning systems with a hope that the use of Semantic Web technologies in educational systems can help the accomplishment of anytime, anywhere, anybody learning, where most of the web resources are reusable learning objects supported by standard technologies and learning is facilitated by intelligent pedagogical agents, that may be adding the essential instructional ingredients implicitly.

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