An Overview and Technological Background of Semantic Technologies

The Semantic Web concept is an extension of the web obtained by adding semantics to the current data representation format. It is considered a network of correlating meanings. It is the result of a combination of web-based conceptions and technologies and knowledge representation. Since the internet has gone through many changes and steps in its web versions 1.0, 2.0, and Web 3.0, this last call of smart web, the concept of Web 3.0, is to be associated with the Semantic Web, since technological advances have allowed the internet to be present beyond the devices that were made exactly with the intention of receiving the connection, not limited to computers or smartphones since it has the concept of reading, writing, and execution off-screen, performed by machines. Therefore, this chapter aims to provide an updated review of Semantic Web and its technologies showing its technological origins and approaching its success relationship with a concise bibliographic background, categorizing and synthesizing the potential of technologies.

Semantic and Blockchain Technology

Blockchain is a distributed ledger with the ability of keeping up the uprightness of exchanges by decentralizing the record among participating clients. The key advancement is that it enables its users to exchange resources over the internet without the requirement for a centralised third party. Also, each 'block' is exceptionally associated with the past blocks by means of digital signature which implies that creation a change to a record without exasperating the previous records in the chain is beyond the realm of imagination, in this way rendering the data tamper-proof. A semantic layer based upon a blockchain framework would join the advantages of adaptable administration disclosure and approval by consensus. This chapter examines the engineering supporting the blockchain and portrays in detail how the information distribution is done, the structure of the block itself, the job of the block header, the block identifier, and the idea of the Genesis block.

NreASAM: Towards an Ontology-Based Model for Authentication and Auto-Grading Online Submission of Psychomotor Assessments

Core and integral to the fourth industrial revolution, knowledge economy, and beyond is information and communication technology (ICT); more so, during and post the novel coronavirus pandemic. Yet, there exists a skills gap in ICT networking and networks engineering. Not only do students perceive ICT networking to be difficult to comprehend, lecturers and institutions grapple with the adequacy of ICT networking equipment. Real-life simulators, like the Cisco Packet Tracer, hold the promise of alternate teaching opportunities and evidenced-based environments for (higher-order) assessment. Research in the last decade on ontology for assessments have focused on taxonomy and multiple-choice questions and auto-generation and marking of assessments. This chapter extends the body of knowledge through its ontology-based model for enabling and auto-assessing performance-based and/or pseudo-psychomotor assessment. The auto-grading online submission system assists with authenticity and enables authentic and/or sustainable assessments.

Structuring Abstraction to Achieve Ontology Modularisation

Large and complex ontologies lead to usage difficulties, thereby hampering the ontology developers' tasks. Ontology modules have been proposed as a possible solution, which is supported by some algorithms and tools. However, the majority of types of modules, including those based on abstraction, still rely on manual methods for modularisation. Toward filling this gap in modularisation techniques, the authors systematised abstractions and selected five types of abstractions relevant for modularisation for which they created novel algorithms, implemented them, and wrapped them in a GUI, called NOMSA, to facilitate their use by ontology developers. The algorithms were evaluated quantitatively by assessing the quality of the generated modules. The quality of a module is measured by comparing it to the benchmark metrics from an existing framework for ontology modularisation. The results show that the module's quality ranges between average to good, whilst also eliminating manual intervention.

UcEF for Semantic IR

At present, keyword-based techniques allow information retrieval (IR) but are unable to capture the conceptualizations in users' information needs and contents. The response to this has been semantic search computing with commendable success. Surprisingly, it is still difficult to evaluate Semantic IR (SIR) and understand the user contexts. The absence of a standardized cognitive user-centred evaluative paradigm (CUcEP) further exacerbates these challenges. This chapter provides the state-of-the-art on IR and SIR evaluation and a systematic review of contexts. Appropriate user-centred theories and the proposed evaluative framework with its integrated-context, web analytic conception, and related data analytic technique are presented. A descriptive approach is adopted, with the conclusion that multiple contexts are essential in SIR evaluation since “searching by meaning” is a multi-dimensional cognitive conception, hence the need to consider the impact of context dynamicity. Finally, the foregrounded semantic items will be applied to standardize the CUcEP in future.

Towards Semantic Data Integration in Resource-Limited Settings for Decision Support on Gait-Related Diseases

Resource-limited settings (RLS) are characterised by lack of access to adequate resources such as ICT infrastructure, qualified medical personnel, healthcare facilities, and affordable healthcare for common people. The potential for the application of AI and clinical decision support systems in RLS are limited due to these challenges. Towards the improvement of the status quo, this chapter presents the conceptual design of a framework for the semantic integration of health data from multiple sources to facilitate decision support for the diagnosis and treatment of gait-related diseases in RLS. The authors describe how the framework can leverage ontologies and knowledge graphs for semantic data integration to achieve this. The plausibility of the proposed framework and the general imperatives for its practical realisation are also presented.

A Review of the IoT-Based Pervasive Computing Architecture for Microservices in Manufacturing Supply Chain Management

Supply chain coordination needs resource and information sharing between business partners. Recent advances in information and communication technology (ICT) enables the evolution of the supply chain industry to meet the new requirements of information sharing architectures due to globalization of supply chain operations. The advent of the internet of things (IoT) technology has since seen a growing interest in architectural design and adaptive frameworks to promote the connection between heterogenous IoT devices and IoT-based information systems. The most widely preferred software architecture in IoT is the semantic web-based service-oriented architecture (SOA), which aims to provide a loosely coupled systems to leverage the use of IoT services at the middle-ware layer to minimise system integration problems. This chapter reviews existing architectural frameworks for integrating IoT devices and identifies the key areas that require further research for industrial information service improvements. Finally, several future research directions in microservice systems are discussed.

Semantic Medical Image Analysis

The recent advancement in imaging technology, together with the hierarchical feature representation capability of deep learning models, has led to the popularization of deep learning models. Thus, research tends towards the use of deep neural networks as against the hand-crafted machine learning algorithms for solving computational problems involving medical images analysis. This limitation has led to the use of features extracted from non-medical data for training models for medical image analysis, considered optimal for practical implementation in clinical setting because medical images contain semantic contents that are different from that of natural images. Therefore, there is need for an alternative to cross-domain feature-learning. Hence, this chapter discusses the possible ways of harnessing domain-specific features which have semantic contents for development of deep learning models.

The Impact of Deep Learning on the Semantic Machine Learning Representation

Deep learning (DL) is one of the core subsets of the semantic machine learning representations (SMLR) that impact on discovering multiple processing layers of non-linear big data (BD) transformations with high levels of abstraction concepts. The SMLR can unravel the concealed explanation characteristics and modifications of the heterogeneous data sources that are intertwined for further artificial intelligence (AI) implementations. Deep learning impacts high-level abstractions in data by deploying hierarchical architectures. It is practically challenging to model big data representations, which impacts on data and knowledge-based representations. Encouraged by deep learning, the formal knowledge representation has the potential to influence the SMLR process. Deep learning architecture is capable of modelling efficient big data representations for further artificial intelligence and SMLR tasks. This chapter focuses on how deep learning impacts on defining deep transfer learning, category, and works based on the techniques used on semantic machine learning representations.

Creation of Value-Added Services by Retrieving Information From Linked and Open Data Portals

In recent decades, different initiatives have emerged in public and private institutions with the aim of offering free access to the data generated in their activity to anyone. In particular, there are two types of initiatives: open data portals and linked data portals. Open data portals are characterized in that it offers access to its content in the form of a REST-type web services API that acts as a query language. On the other hand, linked data portals are characterized in that its data is represented using ontologies encoded by RDF triplets of the subject-predicate-object style forming a knowledge graph. This chapter presents a set of value-added service creation cases using the information stored in open data and linked data repositories. The objective is to show the possibilities offered by the exploitation of these repositories in various fields such as education, tourism, or services such as the search for taxis at an airport.