Since the development of the semantic web, knowledge graphs (KGs) have been used by search engines, knowledge-engines and question-answering services as well as social networks. A knowledge graph, also known as a semantic network, represents and illustrates a network of real-world entities such as objects, events, situations, or concepts and the relationships between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term "knowledge graph". Knowledge graphs structure the information of entities, their properties and the relation between them.

Personal knowledge graphs (PKG) encode the same information at an individual level and therefore vary widely. PKGs require the processing of each person's individual information and is constructed in an automated fashion. Once a PKG is constructed, it will be integrated in broader purpose KGs. A PKG is a representation of all relevant common-sense knowledge and personal data for a user and can support the development of innovative applications such as a digitalized personalized coach. It empowers stakeholders to make more effective decisions.

This book explores in a structured manner the global advanced research around PKGs to support the development of innovative digitalized personalized applications such as personal banking, personalized book-keeping, daily health-related activities monitoring and goal management tracking. The authors present methodologies, tools and applications including innovative topics tailored for PKGs such as named entity recognition and linking, construction approaches, modelling of personalization and context-awareness, evaluation approaches, relation extraction techniques, query answering in user specific knowledge graphs, knowledge representation and reasoning (KRR), visualization tools, integration tools and techniques, and fact summarization.

The book provides systematic coverage of this complex topic for researchers, scientists and engineers in both industry and academia working in data science, ICTs, knowledge engineering, semantic web, reasoning, information retrieval, and machine and deep learning with a focus on knowledge graphs. Advanced students with an interest in the field will also find this to be a useful resource.

As wearable microelectronics are becoming ubiquitous, there is a growing interest in replacing batteries with a means of harnessing power from the user's environment via embedded systems. Efforts have been made to prolong the harvester's operational lifetime, overcoming energy dissipation, lowering resonant frequency, attaining multi-resonant states, and widening the operating frequency bandwidth of the biomechanical energy harvesters. Such technological advances mean harvesting energy is a viable solution for sustainably powering wearable electronics for health and wellbeing applications, such as continuous medical health monitoring, remote sensing, and motion tracking.

The book introduces the concepts of vibration-based piezoelectric, electromagnetic and hybrid energy harvesters, and addresses their modelling, fabrication and characterization. It covers the fundamental principles and details the most advanced functions, including biomechanical and space applications. Detailed descriptions and explanations of a wide range of related concepts are provided, such as multi-degrees of freedom hybrid piezo-electromagnetic insole energy harvesters, non-linear 3D printed electromagnetic vibration energy harvesters, and finite element analysis of hybrid piezoelectric and electromagnetic energy harvesting. Also included are trends towards design, modelling, fabrication, and characterization of nonlinear multimodal electromagnetic and hybrid piezo-electromagnetic insole energy harvesters, as well as describing and explaining electromagnetic and hybrid piezo-electromagnetic energy harvesting technologies. The book provides an extensive and up-dated survey of the published scientific and technical articles and conference reports, covering more than 340 references. The book concludes with an outlook from the authors on likely future developments and applications.

Energy Harvesting for Wireless Sensing and Flexible Electronics through Hybrid Technologies provides in-depth coverage of the topic for researchers from academia and industry, as well as advanced students with an interest in the field.