It's estimated that U.S. companies spend over $14 billion annually on leadership development --Match that number to the abundant and growing research that finds most leadership development to be ineffective, and the conclusion is a phenomenal amount of waste. The remedy for this situation is to have business strategy drive leadership development instead of creating programs that match a one-size-fits-all approach to leadership. This book's approach, called Strategy-Driven Leadership Development (SDLD), puts business strategy first. It maintains an emphasis on building leadership programs around what it will take to make the business successful as opposed to implementing a program in the hopes that it will benefit the strategy. The result is a differentiated and targeted approach called Intentional Leadership Development, which provides the structure for transforming how leadership development is undertaken. At the heart of this book, however, is the explanation of how small, incremental changes in action and perspective create meaningful changes in the way leadership is developed. The focus is on the leadership behaviors associated with success for any company. Some companies may need leaders with better financial acumen while others may require better teamwork for success. These skills are learnable and when the energy of an organization is behind it, then leadership development can be transformational. The authors method "retools" prior leadership efforts - the emphasis is not on previous failures and restarting with new programs. There are many effective ideas and actions that are currently embedded in leadership programs, but they miss the critical element of tying their efforts to the business strategy. Strategy-Driven Leadership changes the way organizations think about and drive their leadership talent initiatives among their current and upcoming leaders. The book is filled with research, science-based information, case studies, and practical hands-on tools on why and how this Strategy-Driven Leadership Development model will transform company leadership approaches.

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques-based on statistics, graph analytics, machine learning, etc.-can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence.The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities.

This book constitutes the proceedings of the satellite events held at the 17th Extended Semantic Web Conference, ESWC 2020, in May/June 2020. The conference was planned to take place in Heraklion, Crete, Greece, but changed to an online format due to the COVID-19 pandemic. ESWC is a major venue for presenting and discussing the latest scientific results and technology innovations related to the Semantic Web, Linked Data and Knowledge Graphs. The 36 poster and demo papers, 7 PhD symposium papers, and 4 industry track papers, included in this volume were carefully reviewed and selected from 59 submissions to the poster and demo track; 11 submissions to the PhD symposium track, and 5 submissions to the Industry track.

This book constitutes the refereed proceedings of the 17th International Semantic Web Conference, ESWC 2020, held in Heraklion, Crete, Greece.* The 39 revised full papers presented were carefully reviewed and selected from 166 submissions. The papers were submitted to three tracks: the research track, the resource track and the in-use track. These tracks showcase research and development activities, services and applications, and innovative research outcomes making their way into industry. The research track caters for both long standing and emerging research topics in the form of the following subtracks: ontologies and reasoning; natural language processing and information retrieval; semantic data management and data infrastructures; social and human aspects of the Semantic Web; machine learning; distribution and decentralization; science of science; security, privacy, licensing and trust; knowledge graphs; and integration, services and APIs. *The conference was held virtually due to the COVID-19 pandemic. Chapter 'Piveau: A Large-scale Oopen Data Management Platform based on Semantic Web Technologies' is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.