Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field

Knowledge Representation in Engineering 4.0

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp- fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern. Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web- datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern von implizitem Wissen und Regeln unterstützen. Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer, für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort- geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft. Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason- ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Computational and human-based methods for knowledge discovery over knowledge graphs

The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database $DS$ can derive new statements to ego networks given an abstract target prediction. Thus, $DS$ minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Improving data management through automatic information extraction model in ontology for road asset management

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas