Название: Domain-Specific Knowledge Graph Construction Автор: Mayank Kejriwal Издательство: Springer Серия: SpringerBriefs in Computer Science Год: 2019 Язык: английский Формат: pdf (true), epub Размер: 15.6 MB
In recent years, knowledge graphs (KGs) have emerged as a major area in Artificial Intelligence (AI). Graphs have always been pervasive in the broader AI literature, but with the advent of large quantities of data on the Web ("Big Data") and in the broader commercial sphere, there emerged a need to enable machines to ‘understand’ and make use of this data in some productive analytical way. The inability of machines to truly understand English, and other ‘natural’ languages like it, with all their irregularities and nuances, has also been largely evident in the (unsuccessful) quest to achieve general AI and commonsense reasoning. Although much progress has been made in all of these domains, it is still very much the case that machines have an easier time processing structured data in the form of graphs, dictionaries and tables than in natural language.
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 (ML) 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 will describe 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 work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”.
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Domain-Specific Knowledge Graph Construction 
