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

Insight ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 15-20 ◽  
Author(s):  
Thomas Hagedorn ◽  
Mary Bone ◽  
Benjamin Kruse ◽  
Ian Grosse ◽  
Mark Blackburn
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Systems Engineering ◽  
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.

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

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.

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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