The Knowledge Grid is an intelligent and sustainable interconnection environment that consists of autonomous individuals, self-organized semantic communities, adaptive networking mechanisms, evolving semantic link networks keeping meaningful connection between individuals, flows for dynamic resource sharing, and mechanisms supporting effective resource management and providing appropriate knowledge services for learning, innovation, teamwork, problem solving, and decision making. This book presents its methodology, theory, models and applications systematically for the first time. Its second edition fulfills the ideal of the Knowledge Grid by increasing many up-to-date new contents, including: the systematic method of semantic link network that supports uncertainty management, discovery of semantic links and semantic communities, and autonomous semantic data model; semantic peer-to-peer infrastructures for efficient knowledge sharing; and, a new centrality measure of network and its application in e-science. This new edition will undoubtedly provide refreshing materials for researchers, academics, practitioners and students.
A straightforward guide to optimizing information assets in the networked enterprise, this book includes extended, unpublished case studies from major industry with thorough implementation guidance. The text is supported by many tables, flow charts and graphical models.
There are millions of searchable data sources on the Web and to a large extent their contents can only be reached through their own query interfaces. There is an enormous interest in making the data in these sources easily accessible. There are primarily two general approaches to achieve this objective. The first is to surface the contents of these sources from the deep Web and add the contents to the index of regular search engines. The second is to integrate the searching capabilities of these sources and support integrated access to them. In this book, we introduce the state-of-the-art techniques for extracting, understanding, and integrating the query interfaces of deep Web data sources. These techniques are critical for producing an integrated query interface for each domain. The interface serves as the mediator for searching all data sources in the concerned domain. While query interface integration is only relevant for the deep Web integration approach, the extraction and understanding of query interfaces are critical for both deep Web exploration approaches.
This book aims to provide in-depth and comprehensive coverage of the key technologies needed to create high quality integrated query interfaces automatically. The following technical issues are discussed in detail in this book: query interface modeling, query interface extraction, query interface clustering, query interface matching, query interface attribute integration, and query interface integration.
Table of Contents: Introduction / Query Interface Representation and Extraction / Query Interface Clustering and Categorization / Query Interface Matching / Query Interface Attribute Integration / Query Interface Integration / Summary and Future Research