Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Trends and Gaps in Ontology-Supported Environmental Health

Environmental Health (EH) refers to aspects of human health affectedby factors in the environment, e.g., biological factors, andit is an essential part of any comprehensive public health system.Similar to other health-related fields, one observes an increasingmovement in the adoption of IoT technologies into the EH domain.Regarding the data life cycle in IoT systems, data modeling andinterpretation are crucial tasks in which ontologies are a feasiblesolution because of their expressiveness and reasoning support.In this paper, we structure the ontology-supported EH researchtheme through a systematic literature mapping. The identificationand selection strategies of primary studies include the automaticsearch for studies published from 2010 to 2019 on five sourcesand the application of inclusion and exclusion criteria on an eighthundred-eleven-distinct-paper group. The results of this originalwork provide an overview of the research theme with multipleclassifications of thirty-four relevant studies remaining as well asthe finding of trends and gaps for future work

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Command and Control Systems for Search and Rescue Robots

The novel application of unmanned systems in the domain of humanitarian Search and Rescue (SAR) operations has created a need to develop specific multi-Robot Command and Control (RC2) systems. This societal application of robotics requires human-robot interfaces for controlling a large fleet of heterogeneous robots deployed in multiple domains of operation (ground, aerial and marine). This chapter provides an overview of the Command, Control and Intelligence (C2I) system developed within the scope of Integrated Components for Assisted Rescue and Unmanned Search operations (ICARUS). The life cycle of the system begins with a description of use cases and the deployment scenarios in collaboration with SAR teams as end-users. This is followed by an illustration of the system design and architecture, core technologies used in implementing the C2I, iterative integration phases with field deployments for evaluating and improving the system. The main subcomponents consist of a central Mission Planning and Coordination System (MPCS), field Robot Command and Control (RC2) subsystems with a portable force-feedback exoskeleton interface for robot arm tele-manipulation and field mobile devices. The distribution of these C2I subsystems with their communication links for unmanned SAR operations is described in detail. Field demonstrations of the C2I system with SAR personnel assisted by unmanned systems provide an outlook for implementing such systems into mainstream SAR operations in the future

Service-oriented design of environmental information systems

Service-orientation has an increasing impact upon the design process and the architecture of environmental information systems. This thesis specifies the SERVUS design methodology for geospatial applications based upon standards of the Open Geospatial Consortium. SERVUS guides the system architect to rephrase use case requirements as a network of semantically-annotated requested resources and to iteratively match them with offered resources that mirror the capabilities of existing services

Emerging Informatics

The book on emerging informatics brings together the new concepts and applications that will help define and outline problem solving methods and features in designing business and human systems. It covers international aspects of information systems design in which many relevant technologies are introduced for the welfare of human and business systems. This initiative can be viewed as an emergent area of informatics that helps better conceptualise and design new world-class solutions. The book provides four flexible sections that accommodate total of fourteen chapters. The section specifies learning contexts in emerging fields. Each chapter presents a clear basis through the problem conception and its applicable technological solutions. I hope this will help further exploration of knowledge in the informatics discipline

The role of semantic web technologies for IoT data in underpinning environmental science

The advent of Internet of Things (IoT) technology has the potential to generate a huge amount of heterogeneous data at different geographical locations and with various temporal resolutions in environmental science. In many other areas of IoT deployment, volume and velocity dominate, however in environmental science, the more general pattern is quite distinct and often variety dominates. There exists a large number of small, heterogeneous and potentially complex datasets and the key challenge is to understand the interdependencies between these disparate datasets representing different environmental facets. These characteristics pose several data challenges including data interpretation, interoperability and integration, to name but a few, and there is a pressing need to address these challenges. The author postulates that Semantic Web technologies and associated techniques have the potential to address the aforementioned data challenges and support environmental science. The main goal of this thesis is to examine the potential role of Semantic Web technologies in making sense of such complex and heterogeneous environmental data in all its complexity. The thesis explores the state-of-the-art in the use of such technologies in the context of environmental science. After an in-depth assessment of related work, the thesis further examined the characteristics of environmental data through semi-structured interviews with leading experts. Through this, three key research challenges emerge: discovering interdependencies between disparate datasets, geospatial data integration and reasoning, and data heterogeneity. In response to these challenges, an ontology was developed that semantically enriches all sensor measurements stemmed from an experimental Environmental IoT infrastructure. The resultant ontology was evaluated through three real-world use-cases derived from the interviews. This led to a number of major contributions from this work including: the development of an ontology tailored for streaming environmental data offering semantic enrichment of IoT data, support for spatio-temporal data integration and reasoning, and the analysis of unique characteristics of environmental science around data

Ontology-based approach to semantically enhanced question answering for closed domain: a review

Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain