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Author : Pieter Hendrik de Vries
Publisher : Unknown
Page : 172 pages
File Size : 45,5 Mb
Release : 1989
Category : Conceptual structures (Information theory)
ISBN : STANFORD:36105043305643
Author : Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d’Amato,Gerard de Melo,Claudio Gutierrez,Sabrina Kirrane,Jose Emilio Labra Gayo,Roberto Navigli,Sebastian Neumaier,Axel Polleres,Sabbir Rashid,Anisa Rula,Antoine Zimmermann,Lukas Schmelzeisen,Axel-Cyrille Ngonga Ngomo,Juan Sequeda,Steffen Staab
Publisher : Springer Nature
Page : 247 pages
File Size : 47,8 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031019180
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 : Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. McGuinness
Publisher : Springer Nature
Page : 110 pages
File Size : 50,9 Mb
Release : 2021-04-26
Category : Computers
ISBN : 9783030676810
RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Author : Valentina Janev,Damien Graux,Hajira Jabeen,Emanuel Sallinger
Publisher : Springer Nature
Page : 212 pages
File Size : 41,7 Mb
Release : 2020-07-15
Category : Computers
ISBN : 9783030531997
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 : Gerard Ellis
Publisher : Springer Science & Business Media
Page : 372 pages
File Size : 41,5 Mb
Release : 1995-07-21
Category : Computers
ISBN : 3540601619
This book constitutes the proceedings of the Third International Conference on Conceptual Structures, ICCS '95, held in Santa Cruz, California in August 1995. Conceptual structures are a modern treatment of Peirce's existential graphs, a graphic notation for classical logic with higher order extensions. Besides three invited papers, there are included 21 revised full papers selected from 58 submission. The volume reflects the state-of-the-art in this research area of growing interest. The papers are organized in sections on natural language, applications, programming in conceptual graphs, machine learning and knowledge acquisition, hardware and implementation, graph operations, and ontologies and theory.
Author : Qinglei Zhou,Qiguang Miao,Hongzhi Wang,Wei Xie,Yan Wang,Zeguang Lu
Publisher : Springer
Page : 676 pages
File Size : 47,8 Mb
Release : 2018-09-10
Category : Computers
ISBN : 9789811322068
This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.
Author : Mayank Kejriwal,Craig A. Knoblock,Pedro Szekely
Publisher : MIT Press
Page : 559 pages
File Size : 53,7 Mb
Release : 2021-03-30
Category : Computers
ISBN : 9780262045094
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 : Roger Lee
Publisher : Springer Nature
Page : 224 pages
File Size : 52,8 Mb
Release : 2022-11-22
Category : Technology & Engineering
ISBN : 9783031121272
This book presents scientific results of the 22nd IEEE/ACIS International Conference on Computer and Information Science (ICIS 2022) held on June 26-28, 2022 in Zhuhai China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications, and tools) of computer and information science and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 14 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.
Author : Han Qiu,Cheng Zhang,Zongming Fei,Meikang Qiu,Sun-Yuan Kung
Publisher : Springer Nature
Page : 679 pages
File Size : 50,8 Mb
Release : 2021-08-07
Category : Computers
ISBN : 9783030821470
This three-volume set constitutes the refereed proceedings of the 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, held in Tokyo, Japan, in August 2021. The 164 revised full papers were carefully reviewed and selected from 492 submissions. The contributions are organized in the following topical sections: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management with optimization and security.
Author : I. Tiddi,F. Lécué,P. Hitzler
Publisher : IOS Press
Page : 314 pages
File Size : 42,9 Mb
Release : 2020-05-06
Category : Computers
ISBN : 9781643680811
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Author : Gerrit C. van der Veer,Gijsbertus Mulder
Publisher : Springer Science & Business Media
Page : 466 pages
File Size : 48,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642734021
This book provides a broad overview of the contributions of experimental research in psychology and related disciplines to the domain of human-computer interaction. Four major topics are considered. The first deals with the presentation of visual information and basic aspects of visual information processing. Some relevant applications are also illustrated in the domains of texts and visual presentation of statistical information. The second major topic is concerned with the representation of knowledge. The interaction between man and machine is most effective if both components have an adequate representation of knowledge. Several techniques of representation are shown, and the compatibility between human representation and machine representation is discussed. The development of expert systems will in many respects change the nature of the interaction between man and machine in artificial intelligence. In the third part, future developments, the current state of expert systems as compared with human experts and the characteristics of productions systems which are so prominent in most expert systems are all discussed. Finally, some features of interaction with systems are reviewed, including the ergonomic value of key boards and advanced input modes like handwritten text and speech. Procedures for searching for information in large databases and for the use of natural language in the interaction between man and machine are increasingly important.
Author : Zhiyuan Liu,Yankai Lin,Maosong Sun
Publisher : Springer Nature
Page : 319 pages
File Size : 52,7 Mb
Release : 2020-07-03
Category : Computers
ISBN : 9789811555732
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Author : Haofen Wang,Xianpei Han,Ming Liu,Gong Cheng,Yongbin Liu,Ningyu Zhang
Publisher : Springer Nature
Page : 371 pages
File Size : 42,9 Mb
Release : 2023-11-28
Category : Computers
ISBN : 9789819972241
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 : Gerard Memmi,Baijian Yang,Linghe Kong,Tianwei Zhang,Meikang Qiu
Publisher : Springer Nature
Page : 780 pages
File Size : 53,7 Mb
Release : 2022-07-19
Category : Computers
ISBN : 9783031109836
The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6–8, 2022. The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS)
Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 55,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
