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Semantic Models for Multimedia Database Searching and Browsing (Advances in Database Systems #21)

Semantic Models for Multimedia Database Searching and Browsing begins with the introduction of multimedia information applications, the need for the development of the multimedia database management systems (MDBMSs), and the important issues and challenges of multimedia systems. The temporal relations, the spatial relations, the spatio-temporal relations, and several semantic models for multimedia information systems are also introduced. In addition, this book discusses recent advances in multimedia database searching and multimedia database browsing. More specifically, issues such as image/video segmentation, motion detection, object tracking, object recognition, knowledge-based event modeling, content-based retrieval, and key frame selections are presented for the first time in a single book. Two case studies consisting of two semantic models are included in the book to illustrate how to use semantic models to design multimedia information systems. Semantic Models for Multimedia Database Searching and Browsing is an excellent reference and can be used in advanced level courses for researchers, scientists, industry professionals, software engineers, students, and general readers who are interested in the issues, challenges, and ideas underlying the current practice of multimedia presentation, multimedia database searching, and multimedia browsing in multimedia information systems.

Semantic Multimedia: First International Conference on Semantic and Digital Media Technologies, SAMT 2006, Athens, Greece, December 6-8, 2006, Proceedings (Lecture Notes in Computer Science #4306)

This book constitutes the refereed proceedings of the First International Conference on Semantics and Digital Media Technologies, SAMT 2006, held in Athens, Greece in December 2006. The 17 revised full papers address a wide area of integrative research on new knowledge-based forms of digital media systems, semantics, and low-level multimedia processing.

Semantic Multimedia: 4th International Conference on Semantic and Digital Media Technologies, SAMT 2009 Graz, Austria, December 2-4, 2009 Proceedings (Lecture Notes in Computer Science #5887)

This volume contains the full and short papers of SAMT 2009, the 4th Int- national Conference on Semantic and Digital Media Technologies 2009 held in Graz, Austria. SAMT brings together researchers dealing with a broad range of research topics related to semantic multimedia and a great diversity of application - eas. The current research shows that adding and using semantics of multimedia content is broadening its scope from search and retrieval to the complete media life cycle, from content creation to distribution and consumption, thus lever- ing new possibilities in creating, sharing and reusing multimedia content. While some of the contributions present improvements in automatic analysis and - notation methods, there is increasingly more work dealing with visualization, user interaction and collaboration. We can also observe ongoing standardization activities related to semantic multimedia in both W3C and MPEG, forming a solid basis for a wide adoption. Theconferencereceived41submissionsthisyear,ofwhichtheProgramC- mittee selected 13 full papers for oral presentation and 8 short papers for poster presentation. In addition to the scienti?c papers, the conference program - cluded two invited talks by Ricardo Baeza-Yates and Stefan Rug ¨ er and a demo session showing results from three European projects. The day before the main conference o?ered an industry day with presen- tions and demos that showed the growing importance of semantic technologies in real-world applications as well as the research challenges coming from them.

Semantic Multimedia: Third International Conference on Semantic and Digital Media Technologies, SAMT 2008, Koblenz, Germany, December 3-5, 2008. Proceedings (Lecture Notes in Computer Science #5392)

WearepleasedtowelcomeyoutotheproceedingsoftheThirdInternationalC- ference onSemantic andDigital Media Technologiesheld inKoblenz,Germany. The SAMT agenda brings together researchers at extreme ends of the - mantic multimedia spectrum. At one end, the Semantic Web and its supporting technologies are becoming established in both the open data environment and within specialist domains, such as corporate intranet search, e-Science (parti- larly life sciences), and cultural heritage. To facilitate the world-wide sharing of media, W3C is developing standard ways of denoting fragments of audio/visual content and of specifying and associating semantics with these. At the other end of the spectrum, media analysis tools continue to grow in sophistication, identifying features that can then be associated with explicit semantics, be they expressed formally or informally, using proprietary formats or open standards. Recent progress at these two fronts of the SAMT spectrum means that research spanningthesemanticgapisnowofvitalimportancetofeedtherealapplications that are emerging. This conference also represents a step towards bridging the gap between the research cultures and their respective approaches at both ends of the spectrum. The papers selected show that SAMT is able to attract researchers from media analysis, who see the bene?ts that more explicit semantics can provide, as well as researchers from knowledge engineering who realize that, while a picture can be expressed as a thousand concepts, a million morearewaiting to be extracted.

Semantic Multimedia: Second International Conference on Semantic and Digital Media Technologies, SAMT 2007, Genoa, Italy, December 5-7, 2007, Proceedings (Lecture Notes in Computer Science #4816)

This book constitutes the refereed proceedings of the Second International Conference on Semantics and Digital Media Technologies, SAMT 2007, held in Genoa, Italy, in December 2007. The conference brings together forums, projects, institutions and individuals investigating the integration of knowledge, semantics and low-level multimedia processing, including new emerging media and application areas. The papers are organized in topical sections.

Semantic Multimedia: 5th International Conference on Semantic and Digital Media Technologies, SAMT 2010, Saarbrücken, Germany, December 1-3, 2010, Revised Selected Papers (Lecture Notes in Computer Science #6725)

This book constitutes the revised selected papers of the 5th International Conference on Semantics and Digital Media Technologies, SAMT 2010, held in Saarbrücken, Germany, in December 2010. As a result of a highly selective review procedure, 12 full papers and 4 short papers were accepted for publication. The contributions present novel approaches for managing, distributing and accessing large amounts of multimedia material. The topics covered include semantic search, analysis and retrieval of images, audio, video, 3D/4D material as well as of computer generated multimedia content. Also addressed are issues relating to semantic metadata management, semantic user interfaces, and semantics in visualization and computer graphics.

Semantic Multimedia Analysis and Processing (Digital Imaging and Computer Vision #9)

Broad in scope, Semantic Multimedia Analysis and Processing provides a complete reference of techniques, algorithms, and solutions for the design and the implementation of contemporary multimedia systems. Offering a balanced, global look at the latest advances in semantic indexing, retrieval, analysis, and processing of multimedia, the book features the contributions of renowned researchers from around the world. Its contents are based on four fundamental thematic pillars: 1) information and content retrieval, 2) semantic knowledge exploitation paradigms, 3) multimedia personalization, and 4) human-computer affective multimedia interaction. Its 15 chapters cover key topics such as content creation, annotation and modeling for the semantic web, multimedia content understanding, and efficiency and scalability. Fostering a deeper understanding of a popular area of research, the text: Describes state-of-the-art schemes and applications Supplies authoritative guidance on research and deployment issues Presents novel methods and applications in an informative and reproducible way Contains numerous examples, illustrations, and tables summarizing results from quantitative studies Considers ongoing trends and designates future challenges and research perspectives Includes bibliographic links for further exploration Uses both SI and US units Ideal for engineers and scientists specializing in the design of multimedia systems, software applications, and image/video analysis and processing technologies, Semantic Multimedia Analysis and Processing aids researchers, practitioners, and developers in finding innovative solutions to existing problems, opening up new avenues of research in uncharted waters.

Semantic Multimedia Analysis and Processing (Digital Imaging and Computer Vision)

Broad in scope, Semantic Multimedia Analysis and Processing provides a complete reference of techniques, algorithms, and solutions for the design and the implementation of contemporary multimedia systems. Offering a balanced, global look at the latest advances in semantic indexing, retrieval, analysis, and processing of multimedia, the book features the contributions of renowned researchers from around the world. Its contents are based on four fundamental thematic pillars: 1) information and content retrieval, 2) semantic knowledge exploitation paradigms, 3) multimedia personalization, and 4) human-computer affective multimedia interaction. Its 15 chapters cover key topics such as content creation, annotation and modeling for the semantic web, multimedia content understanding, and efficiency and scalability. Fostering a deeper understanding of a popular area of research, the text: Describes state-of-the-art schemes and applications Supplies authoritative guidance on research and deployment issues Presents novel methods and applications in an informative and reproducible way Contains numerous examples, illustrations, and tables summarizing results from quantitative studies Considers ongoing trends and designates future challenges and research perspectives Includes bibliographic links for further exploration Uses both SI and US units Ideal for engineers and scientists specializing in the design of multimedia systems, software applications, and image/video analysis and processing technologies, Semantic Multimedia Analysis and Processing aids researchers, practitioners, and developers in finding innovative solutions to existing problems, opening up new avenues of research in uncharted waters.

Semantic Multimedia and Ontologies: Theory and Applications

This comprehensive book draws together experts to explore how knowledge technologies can be exploited to create new multimedia applications, and how multimedia technologies can provide new contexts for the use of knowledge technologies. Thorough coverage of all relevant topics is given. The step-by-step approach guides the reader from fundamental enabling technologies of ontologies, analysis and reasoning, through to applications which have hitherto had less attention.

Semantic Networks for Understanding Scenes (Advances in Computer Vision and Machine Intelligence)

Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background". In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.

Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language (Lecture Notes in Computer Science #6036)

Semantic Relations Between Nominals (Synthesis Lectures on Human Language Technologies)

People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.

Semantic Relations Between Nominals, Second Edition (Synthesis Lectures on Human Language Technologies)

Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, rocks are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including summarization, question answering and machine translation. Semantic relations are the connections we perceive between things which interact. The book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories. A historical perspective takes us back more than 2000 years to their beginnings, and then to developments much closer to our time: various attempts at producing lists of semantic relations, necessary and sufficient to express the interaction between entities/concepts. A look at relations outside context, then in general texts, and then in texts in specialized domains, has gradually brought new insights, and led to essential adjustments in how the relations are seen. At the same time, datasets which encompass these phenomena have become available. They started small, then grew somewhat, then became truly large. The large resources are inevitably noisy because they are constructed automatically. The available corpora—to be analyzed, or used to gather relational evidence—have also grown, and some systems now operate at the Web scale. The learning of semantic relations has proceeded in parallel, in adherence to supervised, unsupervised or distantly supervised paradigms. Detailed analyses of annotated datasets in supervised learning have granted insights useful in developing unsupervised and distantly supervised methods. These in turn have contributed to the understanding of what relations are and how to find them, and that has led to methods scalable to Web-sized textual data. The size and redundancy of information in very large corpora, which at first seemed problematic, have been harnessed to improve the process of relation extraction/learning. The newest technology, deep learning, supplies innovative and surprising solutions to a variety of problems in relation learning. This book aims to paint a big picture and to offer interesting details.

Semantic Role Labeling (Synthesis Lectures on Human Language Technologies)

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Semantic Search over the Web (Data-Centric Systems and Applications)

The Web has become the world’s largest database, with search being the main tool that allows organizations and individuals to exploit its huge amount of information. Search on the Web has been traditionally based on textual and structural similarities, ignoring to a large degree the semantic dimension, i.e., understanding the meaning of the query and of the document content. Combining search and semantics gives birth to the idea of semantic search. Traditional search engines have already advertised some semantic dimensions. Some of them, for instance, can enhance their generated result sets with documents that are semantically related to the query terms even though they may not include these terms. Nevertheless, the exploitation of the semantic search has not yet reached its full potential. In this book, Roberto De Virgilio, Francesco Guerra and Yannis Velegrakis present an extensive overview of the work done in Semantic Search and other related areas. They explore different technologies and solutions in depth, making their collection a valuable and stimulating reading for both academic and industrial researchers.The book is divided into three parts. The first introduces the readers to the basic notions of the Web of Data. It describes the different kinds of data that exist, their topology, and their storing and indexing techniques. The second part is dedicated to Web Search. It presents different types of search, like the exploratory or the path-oriented, alongside methods for their efficient and effective implementation. Other related topics included in this part are the use of uncertainty in query answering, the exploitation of ontologies, and the use of semantics in mashup design and operation. The focus of the third part is on linked data, and more specifically, on applying ideas originating in recommender systems on linked data management, and on techniques for the efficiently querying answering on linked data.

Semantic Service Provisioning

Service-oriented computing has recently gained extensive momentum in both industry and academia, and major software vendors hook on to the service paradigm and tailor their software systems towards services in order to accommodate ever-changing process and product requirements in today’s dynamic market environments. While dynamic binding of services at runtime was identified as a core functionality of service-based environments as far back as 2000, its industrial-strength implementation has yet to be achieved. The main reason for this is the lack of rich service specifications, concepts, and tools to process them. This book introduces advanced concepts in service provisioning and service engineering, including semantic concepts, dynamic discovery and composition, and illustrates them in a concrete business use case scenario. To prove the validity of the concepts and technologies, a semantic service provisioning reference architecture framework as well as a prototypical implementation of its subsystems and a prototypical realization of a proper business scenario are presented. Thus the book goes way beyond current service-based software technologies by providing a coherent and consistent set of technologies and systems functionality that realizes advanced concepts in service provisioning. Both the use case scenario and the provisioning platform have already been substantiated and implemented by the EU-funded Adaptive Services Grid project. The book therefore presents state-of-the-art research results that have already passed a real industrial implementation evaluation which is based on the work of over 20 European partners cooperating in the field of semantic service provisioning.

Semantic Similarity from Natural Language and Ontology Analysis (Synthesis Lectures on Human Language Technologies)

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

Semantic Systems. In the Era of Knowledge Graphs: 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings (Lecture Notes in Computer Science #12378)

This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic.

Semantic Systems. The Power of AI and Knowledge Graphs: 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9–12, 2019, Proceedings (Lecture Notes in Computer Science #11702)

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies.

Semantic Techniques for the Web: The REWERSE Perspective (Lecture Notes in Computer Science #5500)

The objective of this state-of-the-art survey is to give a coherent overview of the main topics and results achieved by the Network of Excellence REWERSE on 'Reasoning on the Web', funded by the European Commission and Switzerland within the '6th Framework Programme' (FP6), from 2004 to 2008. The material has been organized into eight chapters, each of which addresses one of the main topics of REWERSE: hybrid reasoning with rules and ontologies, lessons in versatility or how query languages adapt to the Web, evolution and reactivity in the Semantic Web, rule-based policy representations and reasoning, component models for Semantic Web languages, controlled English for reasoning on the Semantic Web, semantic search with GoPubMed, and information integration in bioinformatics with ontologies and standards. Each chapter gives an in-depth coverage of the subject and provides an extensive bibliography with pointers to further literature.

Semantic Technologies for E-Government

E-government faces huge challenges in achieving interoperability and integration, taking into account differences in laws, regulations, services, administrative processes and languages across regions and countries. On the other hand, issues like service, data and process integration have been researched by the Semantic Web community for several years now, and in the last two to three years we have witnessed the first applications of semantic technologies in real, operational e-government systems in both Europe and the US which address exactly these challenges. With this book, the editors present the latest research results on how to use semantic technologies in order to improve or even revolutionize the use of ICT in public administration systems. The contributions are organized into three parts: architectures and process integration, ontologies and interoperability, and portals and user interactions. They give a broad overview of how semantic technologies have been applied in different e-government projects funded from the European program for ICT Research and Development, and they cover a wide spectrum of semantic technologies such as development of domain and service ontologies, semantic enhancements of business process models, semantic Service-Oriented Architectures (SOAs) based on Semantic Web Services (SWS) frameworks, and ontology-based knowledge management. In this volume, researchers of Semantic Web technologies will find a wealth of challenging real-world scenarios to stimulate new fields of research, while developers of e-government systems as well as other stakeholders in public administration will appreciate the detailed presentations and discussions of numerous applications in areas such as e-government portals, personalization of Web-based public services, or integration and orchestration of public administration processes.

Semantic Technologies for Intelligent Industry 4.0 Applications (River Publishers Series in Computing and Information Science and Technology)

As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge,and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very lose to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter. This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people’s future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field.

Semantic Technologies for Intelligent Industry 4.0 Applications (River Publishers Series in Computing and Information Science and Technology)

As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge,and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very lose to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter. This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people’s future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field.

Semantic Technologies in Content Management Systems: Trends, Applications and Evaluations

Content Management Systems (CMSs) are used in almost every industry by millions of end-user organizations. In contrast to the 90s, they are no longer used as isolated applications in one organization but they support critical core operations in business ecosystems. Content management today is more interactive and more integrative: interactive because end-users are increasingly content creators themselves and integrative because content elements can be embedded into various other applications. The authors of this book investigate how Semantic Technologies can increase interactivity and integration capabilities of CMSs and discuss their business value to millions of end-user organizations. This book has therefore the objective, to reflect existing applications as well as to discuss and present new applications for CMSs that use Semantic Technologies. An evaluation of 27 CMSs concludes this book and provides a basis for IT executives that plan to adopt or replace a CMS in the near future.

Semantic Technology: 8th Joint International Conference, JIST 2018, Awaji, Japan, November 26–28, 2018, Proceedings (Lecture Notes in Computer Science #11341)

This book constitutes the thoroughly refereed proceedings of the 8th Joint International Semantic Technology Conference, JIST 2018, held in Awaji, Japan, in November 2018. The 23 full papers and 6 short papers presented were carefully reviewed and selected from 75 submissions. They present applications of semantic technologies, theoretical results, new algorithms and tools to facilitate the adoption of semantic technologies and are organized in topical sections on knowledge graphs; data management; question answering and NLP; ontology and reasoning; government open data; and semantic web for life sciences.