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Author : Georg Rehm
Publisher : Springer Nature
ISBN 13 : 3031657942
Total Pages : 313 pages
Book Rating : 4.0/5 (316 download)
Download or read book Natural Scientific Language Processing and Research Knowledge Graphs written by Georg Rehm and published by Springer Nature. This book was released on with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Georg Rehm
Publisher : Springer
ISBN 13 : 9783031657931
Total Pages : 0 pages
Book Rating : 4.6/5 (579 download)
Download or read book Natural Scientific Language Processing and Research Knowledge Graphs written by Georg Rehm and published by Springer. This book was released on 2024-09-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access book constitutes the refereed proceedings of the First International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs, NSLP 2024, held in Hersonissos, Crete, Greece, on May 27, 2024. The 10 full papers and 11 short papers included in this volume were carefully reviewed and selected from a total of 26 submissions. The proceedings aims to bring together researchers working on the processing, analysis, transformation and making use-of scientific language and research knowledge graphs including all relevant sub-topics.
Author : Aidan Hogan
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1636392369
Total Pages : 257 pages
Book Rating : 4.6/5 (363 download)
Download or read book Knowledge Graphs written by Aidan Hogan and published by Morgan & Claypool Publishers. This book was released on 2021-11-08 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Rada Mihalcea
Publisher : Cambridge University Press
ISBN 13 : 1139498827
Total Pages : 201 pages
Book Rating : 4.1/5 (394 download)
Download or read book Graph-based Natural Language Processing and Information Retrieval written by Rada Mihalcea and published by Cambridge University Press. This book was released on 2011-04-11 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Author : Jose Manuel Gomez-Perez
Publisher : Springer Nature
ISBN 13 : 3030448304
Total Pages : 268 pages
Book Rating : 4.0/5 (34 download)
Download or read book A Practical Guide to Hybrid Natural Language Processing written by Jose Manuel Gomez-Perez and published by Springer Nature. This book was released on 2020-06-16 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.
Author : Muskan Garg
Publisher : CRC Press
ISBN 13 : 1000789306
Total Pages : 272 pages
Book Rating : 4.0/5 (7 download)
Download or read book Graph Learning and Network Science for Natural Language Processing written by Muskan Garg and published by CRC Press. This book was released on 2022-12-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
Author : Mayank Kejriwal
Publisher : MIT Press
ISBN 13 : 0262045095
Total Pages : 559 pages
Book Rating : 4.2/5 (62 download)
Download or read book Knowledge Graphs written by Mayank Kejriwal and published by MIT Press. This book was released on 2021-03-30 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Rajendra Akerkar
Publisher : Springer
ISBN 13 : 9783030481018
Total Pages : 183 pages
Book Rating : 4.4/5 (81 download)
Download or read book Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data written by Rajendra Akerkar and published by Springer. This book was released on 2021-09-16 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume discusses essential topics and the fundamentals for Big Data Emergency Management and primarily focusses on the application of Big Data for Emergency Management. It walks the reader through the state of the art, in different facets of the big disaster data field. This includes many elements that are important for these technologies to have real-world impact. This book brings together different computational techniques from: machine learning, communication network analysis, natural language processing, knowledge graphs, data mining, and information visualization, aiming at methods that are typically used for processing big emergency data. This book also provides authoritative insights and highlights valuable lessons by distinguished authors, who are leaders in this field. Emergencies are severe, large-scale, non-routine events that disrupt the normal functioning of a community or a society, causing widespread and overwhelming losses and impacts. Emergency Management is the process of planning and taking actions to minimize the social and physical impact of emergencies and reduces the community’s vulnerability to the consequences of emergencies. Information exchange before, during and after the disaster periods can greatly reduce the losses caused by the emergency. This allows people to make better use of the available resources, such as relief materials and medical supplies. It also provides a channel through which reports on casualties and losses in each affected area, can be delivered expeditiously. Big Data-Driven Emergency Management refers to applying advanced data collection and analysis technologies to achieve more effective and responsive decision-making during emergencies. Researchers, engineers and computer scientists working in Big Data Emergency Management, who need to deal with large and complex sets of data will want to purchase this book. Advanced-level students interested in data-driven emergency/crisis/disaster management will also want to purchase this book as a study guide.
Author : Radek Silhavy
Publisher : Springer Nature
ISBN 13 : 3030903214
Total Pages : 1073 pages
Book Rating : 4.0/5 (39 download)
Download or read book Data Science and Intelligent Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2021-11-16 with total page 1073 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results
Author : Valentina Janev
Publisher : Springer Nature
ISBN 13 : 3030531996
Total Pages : 212 pages
Book Rating : 4.0/5 (35 download)
Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Leslie F. Sikos
Publisher : Springer Nature
ISBN 13 : 3030676811
Total Pages : 110 pages
Book Rating : 4.0/5 (36 download)
Download or read book Provenance in Data Science written by Leslie F. Sikos and published by Springer Nature. This book was released on 2021-04-26 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Zhiyuan Liu
Publisher : Springer Nature
ISBN 13 : 9811555737
Total Pages : 319 pages
Book Rating : 4.8/5 (115 download)
Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Aman Kumar
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Information Extraction and Knowledge Graph Development for Manufacturing Science Domain Using Natural Language Processing written by Aman Kumar and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Francesco Colace
Publisher : Springer
ISBN 13 : 3319200623
Total Pages : 380 pages
Book Rating : 4.3/5 (192 download)
Download or read book Data Management in Pervasive Systems written by Francesco Colace and published by Springer. This book was released on 2015-10-17 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation. The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.
Author : Sanju Tiwari
Publisher : IET
ISBN 13 : 1839537019
Total Pages : 359 pages
Book Rating : 4.8/5 (395 download)
Download or read book Personal Knowledge Graphs (Pkgs) written by Sanju Tiwari and published by IET. This book was released on 2023-10 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personal knowledge graphs (PKGs) support the development of innovative digitalized personalized applications, which keep users updated, help them manage their day-to-day activities and facilitate informed decisions. This book systematically explores the global advanced research around PKGs from methodologies to tools and applications.
Author : S., Sumathi
Publisher : IGI Global
ISBN 13 : 1799811611
Total Pages : 227 pages
Book Rating : 4.7/5 (998 download)
Download or read book Neural Networks for Natural Language Processing written by S., Sumathi and published by IGI Global. This book was released on 2019-11-29 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.
Author : Gaoyan Ou
Publisher : World Scientific
ISBN 13 : 9811263914
Total Pages : 445 pages
Book Rating : 4.8/5 (112 download)
Download or read book Introduction To Data Science written by Gaoyan Ou and published by World Scientific. This book was released on 2023-11-24 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book systematically introduces the basic contents of data science, including data preprocessing and basic methods of data analysis, handling special problems (e.g. text analysis), deep learning, and distributed systems.In addition to systematically introducing the basic content of data science from a theoretical point of view, the book also provides a large number of data analysis practice cases.
