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This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice.\ It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
This volume contains the lecture notes of the 13th Reasoning Web Summer School, RW 2017, held in London, UK, in July 2017. In 2017, the theme of the school was "Semantic Interoperability on the Web", which encompasses subjects such as data integration, open data management, reasoning over linked data, database to ontology mapping, query answering over ontologies, hybrid reasoning with rules and ontologies, and ontology-based dynamic systems. The papers of this volume focus on these topics and also address foundational reasoning techniques used in answer set programming and ontologies.
Linked Data Management presents techniques for querying and managing Linked Data that is available on today's Web. The book shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications.The text focuses on aspects of managing large-scale collections of Linked Data. It offers a detailed introduction to L
Chapter 2. Organize Data: Design a Robust Architecture for Search -- Organizing Domains in the Data Catalog -- Domain Architecture in a Data Catalog -- Understanding Domains -- Processes and Capabilities -- Data Sources -- Getting Assets into the Data Catalog -- Pull -- Push -- Organizing Assets in the Domains -- Asset Metadata -- Metadata Quality -- Classification -- Summary -- Chapter 3. Understand Search: Concepts, Features, and Mechanics -- Why Do You Search in a Data Catalog? -- Search Features in a Data Catalog -- Searching in Data Versus Searching for Data
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 ...
Vol. 3- includes v. 190- of the Transactions.
Vol. 1 includes "Organization number," published Nov. 1917.