The Semantic Web In Earth And Space Science Current Status And Future Directions

The geosciences are one of the fields leading the way in advancing semantic technologies. This book continues the dialogue and feedback between the geoscience and semantic web communities. Increasing data volumes within the geosciences makes it no longer practical to copy data and perform local analysis. Hypotheses are now being tested through online tools that combine and mine pools of data. This evolution in the way research is conducted is commonly referred to as e-Science. As e-Science has flourished, the barriers to free and open access to data have been lowered and the need for semantics has been heighted. As the volume, complexity, and heterogeneity of data resources grow, geoscientists are creating new capabilities that rely on semantic approaches. Geoscience researchers are actively working toward a research environment of software tools and interfaces to data archives and services with the goals of full-scale semantic integration beginning to take shape. The members of this emerging semantic e-Science community are increasingly in need of semantic-based methodologies, tools and infrastructure. A feedback system between the geo- and computational sciences is forming. Advances in knowledge modeling, logic-based hypothesis checking, semantic data integration, and knowledge discovery are leading to advances in scientific domains, which in turn are validating semantic approaches and pointing to new research directions. We present mature semantic applications within the geosciences and stimulate discussion on emerging challenges and new research directions.

This book constitutes the thoroughly refereed proceedings of the Second Iberoamerican Conference, KGSWC 2020, held in Mérida, Mexico, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 15 papers presented were carefully reviewed and selected from 45 submissions. The papers cover research and practices in several fields of AI, such as knowledge representation and reasoning, natural language processing/text mining, machine/deep learning, semantic web, and knowledge graphs.

New digital technologies have transformed how scientific information is created, disseminated—and discovered. The emergence of new forms of scientific publishing based on open science and open access have caused a major shift in scientific communication and a restructuring of the flow of information. Specialized indexing services and search engines are trying to get into information seekers’ minds to understand what users are actually looking for when typing all these keywords or drawing chemical structures. Using artificial intelligence (AI), machine learning, and semantic indexing, these "discovery agents" are trying to anticipate users’ information needs. In this highly competitive environment, authors should not sit and rely only on publishers, search engines, and indexing services to make their works visible. They need to communicate about their research and reach out to a larger audience. Driving Science Information Discovery in the Digital Age looks through the "eyes" of the main "players" in this "game" and examines the discovery of scientific information from three different, but intertwined, perspectives: Discovering, managing, and using information (Information seeker perspective) Publishing, disseminating, and making information discoverable (Publisher perspective) Creating, spreading, and promoting information (Author perspective). Presents an overview of the current scientific publishing landscape Shows how users can search for scientific information more efficiently Critically analyses the metrics used to measure the quality of journals and the impact of research Looks at the discovery of scientific information from the perspectives of information seekers, publishers, and authors Delves into the practices used by specialized indexing services and search engines to process scientific information and make it discoverable Recommends strategies that authors could use to promote their research

This book provides a comprehensive overview of core concepts and technological foundations for continuous engineering of Web streams. It presents various systems and applications and includes real-world examples. Last not least, it introduces the readers to RSP4J, a novel open-source project that aims to gather community efforts in software engineering and empirical research. The book starts with an introductory chapter that positions the work by explaining what motivates the design of specific techniques for processing data streams using Web technologies. Chapter 2 briefly summarizes the necessary background concepts and models needed to understand the remaining content of the book. Subsequently, chapter 3 focuses on processing RDF streams, taming data velocity in an open environment characterized by high data variety. It introduces query answering algorithms with RSP-QL and analytics functions over streaming data. Chapter 4 presents the life cycle of streaming linked data, it focuses on publishing streams on the Web as a prerequisite aspect to make data findable and accessible for applications. Chapter 5 touches on the problems of benchmarks and systems that analyze Web streams to foster technological progress. It surveys existing benchmarks and introduces guidelines that may support new practitioners in approaching the issue of continuous analytics. Finally, chapter 6 presents a list of examples and exercises that will help the reader to approach the area, get used to its practices and become confident in its technological possibilities. Overall, this book is mainly written for graduate students and researchers in Web and stream data management. It collects research results and will guide the next generation of researchers and practitioners.

To date, a vast amount of the world’s oceans remains uncharted. With water covering more than 70 percent of the Earth’s surface, maritime and oceanographic exploration and research is vital. Oceanography and Coastal Informatics: Breakthroughs in Research and Practice is a critical source of academic knowledge centered on technologies, methodologies, and practices related to the biological and physical aspects of the ocean and coastal environments. This publication is divided into four sections: climate change and environmental concerns; data analysis and management; fisheries management and ecology; and GIS, geospatial analysis, and localization. This publication is an ideal reference source for oceanographers, marine and maritime professionals, researchers, and scholars interested in current research on various aspects of oceanography and coastal informatics.

As human activity makes a greater impact on the environment, sustainability becomes an increasingly imperative goal. With the assistance of current technological innovations, environmental systems can be better preserved. Oceanographic and Marine Cross-Domain Data Management for Sustainable Development is a pivotal resource for the latest research on the collection of environmental data for sustainability initiatives and the associate challenges with this data acquisition. Highlighting various technological, scientific, semantic, and semiotic perspectives, this book is ideally designed for researchers, technology developers, practitioners, students, and professionals in the field of environmental science and technology.

With the increased use of technology in modern society, high volumes of multimedia information exists. It is important for businesses, organizations, and individuals to understand how to optimize this data and new methods are emerging for more efficient information management and retrieval. Information Retrieval and Management: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material in the field of information and communication technologies and explores how complex information systems interact with and affect one another. Highlighting a range of topics such as knowledge discovery, semantic web, and information resources management, this multi-volume book is ideally designed for researchers, developers, managers, strategic planners, and advanced-level students.

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.