Mayank Kejriwal – författare

A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner.The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as anaccessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.

The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner.

The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as anaccessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.

This book provides a brief synthesis of the known implementations, opportunities and challenges at the intersection of artificial intelligence (AI) and modern industry beyond the big-four companies that traditionally consume and produce such advanced technology: Facebook, Amazon, Microsoft and Google. With this information, the author also makes some reasonable claims about the role of AI in future industries. The book draws on a broad range of material, including reports from consulting firms, published surveys, academic papers and books, and expert knowledge available to the author due to numerous collaborations in academia and industry on AI. It is rigorous rather than speculative, drawing on known findings and expert summaries, where available. This provides industry leaders and other interested stakeholders with an accessible review of contemporary perspectives on AI’s forward-looking role in industry as well as a clarifying guide on the major issues that companies are likely to face as they commence on this exciting path.Examines the likely role of AI in industries of the future, both known and unknownPresents use-cases of AI currently being explored across Big Tech, multi-national corporations and start-upsExplores the regulation of AI and its potential impacts on the workforce

This book provides a brief synthesis of the known implementations, opportunities and challenges at the intersection of artificial intelligence (AI) and modern industry beyond the big-four companies that traditionally consume and produce such advanced technology: Facebook, Amazon, Microsoft and Google. With this information, the author also makes some reasonable claims about the role of AI in future industries. The book draws on a broad range of material, including reports from consulting firms, published surveys, academic papers and books, and expert knowledge available to the author due to numerous collaborations in academia and industry on AI. It is rigorous rather than speculative, drawing on known findings and expert summaries, where available. This provides industry leaders and other interested stakeholders with an accessible review of contemporary perspectives on AI’s forward-looking role in industry as well as a clarifying guide on the major issues that companies are likely to face as they commence on this exciting path.

Examines the likely role of AI in industries of the future, both known and unknown

Presents use-cases of AI currently being explored across Big Tech, multi-national corporations and start-ups

Explores the regulation of AI and its potential impacts on the workforce

This book provides a brief synthesis of the known implementations, opportunities and challenges at the intersection of artificial intelligence (AI) and modern industry beyond the big-four companies that traditionally consume and produce such advanced technology: Facebook, Amazon, Microsoft and Google. With this information, the author also makes some reasonable claims about the role of AI in future industries. The book draws on a broad range of material, including reports from consulting firms, published surveys, academic papers and books, and expert knowledge available to the author due to numerous collaborations in academia and industry on AI. It is rigorous rather than speculative, drawing on known findings and expert summaries, where available. This provides industry leaders and other interested stakeholders with an accessible review of contemporary perspectives on AI’s forward-looking role in industry as well as a clarifying guide on the major issues that companies are likely to face as they commence on this exciting path.Examines the likely role of AI in industries of the future, both known and unknownPresents use-cases of AI currently being explored across Big Tech, multi-national corporations and start-upsExplores the regulation of AI and its potential impacts on the workforce

This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid "common sense" reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sense–a grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines.While modern large language models (LLMs) like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of research–from the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybrids–to provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense.A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly "solve" benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores.

This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid "common sense" reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sense–a grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines.While modern large language models (LLMs) like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of research–from the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybrids–to provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense.A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly "solve" benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores.