Author : Juanzi Li
Publisher : Springer
Page : 186 pages
File Size : 24,37 MB
Release : 2018-01-18
Category : Computers
ISBN : 9811073597
This book constitutes the refereed proceedings of the Second China Conference on Knowledge Graph and Semantic Computing, CCKS 2017, held in Chengdu, China, in August 2017. The 11 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 85 submissions. The papers cover wide research fields including the knowledge graph, the Semantic Web, linked data, NLP, knowledge representation, graph databases.
Author : Haofen Wang
Publisher : Springer Nature
Page : 371 pages
File Size : 40,39 MB
Release : 2023-11-28
Category : Computers
ISBN : 9819972248
This book constitutes the refereed proceedings of the 8th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence, CCKS 2023, held in Shenyang, China, during August 24–27, 2023. The 28 full papers included in this book were carefully reviewed and selected from 106 submissions. They were organized in topical sections as follows: knowledge representation and knowledge graph reasoning; knowledge acquisition and knowledge base construction; knowledge integration and knowledge graph management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources; and evaluations.
Author : P. Ristoski
Publisher : IOS Press
Page : 246 pages
File Size : 48,7 MB
Release : 2019-06-28
Category : Computers
ISBN : 1614999813
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.
Author : Huajun Chen
Publisher : Springer Nature
Page : 336 pages
File Size : 16,44 MB
Release : 2021-05-05
Category : Computers
ISBN : 9811619646
This book constitutes the refereed proceedings of the 5th China Conference on Knowledge Graph and Semantic Computing, CCKS 2020, held in Nanchang, China, in November 2020. The 26 revised full papers presented were carefully reviewed and selected from 173 submissions. The papers are organized in topical sections on knowledge extraction: lexical and entity; knowledge extraction: relation; knowledge extraction: event; knowledge applications: question answering, dialogue, decision support, and recommendation.
Author : Valentina Janev
Publisher : Springer Nature
Page : 212 pages
File Size : 10,19 MB
Release : 2020-07-15
Category : Computers
ISBN : 3030531996
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Author : Bing Qin
Publisher : Springer Nature
Page : 339 pages
File Size : 29,68 MB
Release : 2021-10-28
Category : Computers
ISBN : 9811664714
This book constitutes the refereed proceedings of the 6th China Conference on Knowledge Graph and Semantic Computing, CCKS 2021, held in Guangzhou, China, in November 2021. The 19 revised full papers and 9 short papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on knowledge extraction: knowledge graph representation and reasoning; knowledge acquisition and knowledge graph construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications: semantic search, question answering, dialogue, decision support, and recommendation; knowledge graph open resources.
Author : Xiaoyan Zhu
Publisher : Springer Nature
Page : 226 pages
File Size : 15,58 MB
Release : 2020-01-03
Category : Computers
ISBN : 9811519560
This book constitutes the refereed proceedings of the 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019, held in Hangzhou, China, in August 2019. The 18 revised full papers presented were carefully reviewed and selected from 140 submissions. The papers cover wide research fields including the knowledge graph, the semantic Web, linked data, NLP, information extraction, knowledge representation and reasoning.
Author : Mayank Kejriwal
Publisher : MIT Press
Page : 559 pages
File Size : 38,84 MB
Release : 2021-03-30
Category : Computers
ISBN : 0262045095
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.
Author : Aidan Hogan
Publisher : Morgan & Claypool Publishers
Page : 257 pages
File Size : 23,45 MB
Release : 2021-11-08
Category : Computers
ISBN : 1636392369
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.
Author : Dieter Fensel
Publisher : Springer Nature
Page : 156 pages
File Size : 12,22 MB
Release : 2020-01-31
Category : Computers
ISBN : 3030374394
This book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources. Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks. To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation.
