Informationssøgning og informationsgenfinding

This book not only presents the state-of-the-art research on knowledge modelling, knowledge retrieval and knowledge reuse, but also elaborates the Collaborative Knowledge Management (CKM) paradigm and the architecture for the next generation of knowledge management systems. Although knowledge management has been extensively studied, particularly in the fields of business management and engineering design, there is a lack of systematic methodologies for addressing the integrated and collaborative dimension of knowledge management during the collaborative process of designing and developing complex systems, products, processes and services. The rapid development of information technologies, together with their applications in engineering and management, has laid the foundation for a Collaborative Knowledge Management (CKM) paradigm. The book specifically discusses this paradigm from a computational perspective.By exploring specific research findings underpinning further CKM research and applications and describing methods related to hot research topics and new research areas, the book appeals to professionals, researchers and graduate students who are interested in knowledge management and related topics and who have a basic understanding of information technologies, computational methods, and knowledge management.

Genealogies document relationships between persons involved in historical events. Information about the events is parsed from communications from the past. This book explores a way to organize information from multiple communications into a trustworthy representation of a genealogical history of the modern world. The approach defines metrics for evaluating the consistency, correctness, closure, connectivity, completeness, and coherence of a genealogy. The metrics are evaluated using a 312,000-person research genealogy that explores the common ancestors of the royal families of Europe. A major result is that completeness is defined by a genealogy symmetry property driven by two exponential processes, the doubling of the number of potential ancestors each generation, and the rapid growth of lineage coalescence when the number of potential ancestors exceeds the available population. A genealogy expands from an initial root person to a large number of lineages, which then coalesce into a small number of progenitors. Using the research genealogy, candidate progenitors for persons of Western European descent are identified. A unifying ancestry is defined to which historically notable persons can be linked.

The European Summer School in Logic, Language and Information (ESSLLI) is organized every year by the Association for Logic, Language and Information (FoLLI) in different sites around Europe. The papers cover vastly dierent topics, but each fall in the intersection of the three primary topics of ESSLLI: Logic, Language and Computation. The 13 papers presented in this volume have been selected among 81 submitted papers over the years 2019, 2020 and 2021. The ESSLLI Student Session is an excellent venue for students to present their work and receive valuable feedback from renowned experts in their respective fields. The Student Session accepts submissions for three different tracks: Language and Computation (LaCo), Logic and Computation (LoCo), and Logic and Language (LoLa).

This book constitutes the refereed proceedings of the 9th China Health Information Processing Conference, CHIP 2023, held in Hangzhou, China, during October 27¿29, 2023. The 27 full papers included in this book were carefully reviewed and selected from 66 submissions. They were organized in topical sections as follows: healthcare information extraction; healthcare natural language processing; healthcare data mining and applications.

This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied.The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary.This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

This book constitutes the proceedings of the First International Conference, CINS 2023, held in Dubai, United Arab Emirates, from October 18 to 20, 2023.The 11 full papers included in this volume were carefully reviewed and selected from 130 submissions. This volume discusses contemporary challenges within computing systems and the utilization of intelligent approaches to improve computing methodologies, data processing capabilities, and the application of these intelligent techniques. The book also addresses several topics pertaining to networks, including security, network data processing, networks that transcend boundaries, device heterogeneity, and advancements in networks connected to the Internet of Things, software-defined networks, cloud computing, and intelligent networks.

Unlock the Power of Cloud Infrastructure with "IaaS Mastery" Book Bundle!Are you ready to conquer the dynamic world of cloud infrastructure? Look no further than the "IaaS Mastery: Infrastructure as a Service" book bundle, your comprehensive guide to mastering cloud technology with a focus on the industry's leading providers.Discover What's Inside:Book 1 - IaaS Fundamentals: A Beginner's Guide to Cloud Infrastructure Begin your journey with a solid foundation. Learn the essentials of cloud computing and understand the core principles of Infrastructure as a Service. Perfect for newcomers and those seeking a refresher on cloud basics.Book 2 - Mastering IaaS: Building Scalable Cloud Solutions with AWS and GCE Dive into practical applications with Amazon Web Services (AWS) and Google Cloud Engine (GCE). Gain hands-on experience in creating scalable and resilient cloud solutions using these renowned platforms.Book 3 - Advanced IaaS Architectures: Optimizing Microsoft Azure for Enterprises Elevate your expertise with a deep dive into Microsoft Azure. Explore advanced topics such as high-performance networks, scalable compute solutions, security measures, and automation tailored for enterprise workloads.Book 4 - IaaS Expertise: Harnessing the Power of IBM Cloud for Enterprise Solutions Unleash the potential of IBM Cloud as a tool for enterprise transformation. Discover infrastructure offerings, networking strategies, security features, and advanced automation capabilities. Real-world enterprise success stories provide valuable insights.Why Choose "IaaS Mastery"?· Comprehensive Coverage: From beginner to expert, this bundle covers everything you need to know about IaaS.· Hands-On Experience: Practical examples and real-world scenarios ensure you're ready to apply your knowledge.· Top Cloud Providers: Learn from the best-AWS, GCE, Microsoft Azure, and IBM Cloud.· Enterprise Focus: Equip yourself with skills tailored for the demands of large organizations.· Future-Proof Your Career: Cloud technology is the future; stay ahead of the curve with this invaluable resource.Don't miss this opportunity to become an IaaS expert. Whether you're an IT professional, aspiring cloud enthusiast, or business leader, "IaaS Mastery" empowers you with the knowledge and skills to succeed in the digital age. Purchase the bundle today and embark on your journey to mastering cloud infrastructure!

This book explores provenance, the study and documentation of how things come to be.  Traditionally defined as the origins, source, or ownership of an artifact, provenance today is not limited to historical domains.  It can be used to describe what did happen (retrospective provenance), what could happen (subjunctive provenance), or what will happen (prospective provenance). Provenance information is ubiquitous and abundant; for example, a wine label that details the winery, type of grape, and country of origin tells a provenance story that determines the value of the bottle.  This book presents select standards used in organizing provenance information and provides concrete examples on how to implement them.  Provenance transcends disciplines, and this book is intended for anyone who is interested in documenting workflows and recipes.  The goal is to empower readers to frame and answer provenance questions for their own work.  Provenance is increasingly important in computational workflows and e-sciences and addresses the need for a practical introduction to provenance documentation with simple-to-use multi-disciplinary examples and activities.  Case studies and examples address the creation of basic records using a variety of provenance metadata models, and the differences between PROV, ProvONE, and PREMIS are discussed.  Readers will gain an understanding of the uses of provenance metadata in different domains and sectors in order to make informed decisions on their use.  Documenting provenance can be a daunting challenge, and with clear examples and explanations, the task will be less intimidating to explore provenance needs.

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.

This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios.  In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling.  Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years.  Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items.  This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets.  This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items.  To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.  Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.  

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning.After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9.This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

This book constitutes the post-conference proceedings of the satellite events held at the 20th Extended Semantic Web Conference, ESWC 2023, held in Hersonissos, Greece, during May 28¿June 1, 2023.The 50 full papers included in this book were carefully reviewed and selected from 109 submissions. They were organized in sections as follows: Posters and Demos, Industry, and PhD Symposium.

This book constitutes the refereed proceedings of the 19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), in November 2023, in Jakarta, Indonesia. The 9 full papers and 2 short papers included in this volume were carefully reviewed and selected from 28 initial submissions. They are organized in the topical section such as machine learning, natural language processing, and intelligent systems.

This book constitutes the thoroughly refereed proceedings of the 11th Chinese National Conference of Social Media Processing, SMP 2023, held in Anhui, China, in November 2023.The 16 full papers presented were carefully reviewed and selected from 88 submissions. The papers are organized in the topical sections on knowledge representation and reasoning; knowledge acquisition and knowledge base construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources.

This book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today's information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven't yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.

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.

The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.