This book constitutes the thoroughly refereed post conference proceedings of the 4th edition of the Semantic Web Evaluation Challenge, SemWebEval 2018, co-located with the 15th European Semantic Web conference, held in Heraklion, Greece, in June 2018. This book includes the descriptions of all methods and tools that competed at SemWebEval 2018, together with a detailed description of the tasks, evaluation procedures and datasets. The 18 revised full papers presented in this volume were carefully reviewed and selected from 24 submissions. The contributions are grouped in the areas: the mighty storage challenge; open knowledge extraction challenge; question answering over linked data challenge; semantic sentiment analysis.

This book constitutes the thoroughly refereed post conference proceedings of the first edition of the Semantic Web Evaluation Challenge, SemWebEval 2014, co-located with the 11th Extended Semantic Web conference, held in Anissaras, Crete, Greece, in May 2014. This book includes the descriptions of all methods and tools that competed at SemWebEval 2014, together with a detailed description of the tasks, evaluation procedures and datasets. The contributions are grouped in three areas: semantic publishing (sempub), concept-level sentiment analysis (ssa), and linked-data enabled recommender systems (recsys).

Energy efficiency is being improved in the Internet equipment. This is becoming an increasingly important research topic, motivated by the need to reduce energy costs for Internet Service Providers, as well as increase power density to achieve more switching capacity per-rack. In this work we consider the NetFPGA platform, which is becoming an increasingly popular routing platform for networking research due to its versatility and low-cost.

Internet protocols and technology, even if originally conceived for data communication, can actually be used even for real-time video transport. Progress made in image-processing, as in computer and networking technology, allowed the implementation of several useful applications, such as video streaming, video conference and video phone calling. Specifically, the work done for this book focuses upon multipoint real time Mpeg4-FGS video distribution with a P2p approach.

In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word sense disambiguators - systems that given a word appearing in a certain context, can identify the sense of that word. Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem, that is, a problem which can be solved only by first resolving all the difficult problems in artificial intelligence (AI), such as the representation of common sense and encyclopedic knowledge. Moreover, it has been found that people are inconsistent when asked to disambiguate words and this causes problems when testing the output of an automatic disambiguator. A breakthrough in this field would have a significant impact on many relevant web-base applications, such as information retrieval and information extraction. In the review of disambiguation research, many varied techniques for performing automatic disambiguation are introduced. In this book, three different language independent strategies for word sense disambiguation are proposed and evaluated. The performances of the resulting system have been compared with respect to other well know methods for word sense disambiguation.

There has been increasing interest in building search/index structures to perform similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. A similarity search problem involves a collection of objects (e.g., documents, images) which are characterized by a collection of relevant features and represented as points in a high-dimensional attribute space. The first part of this book will present a new hierarchical clustering algorithm called Antipole Clustering. The algorithm partitions the set of data objects in clusters such that each one has diameter approximately less than a given value. The algorithm returns a tree structure called Antipole Tree in which the leaves are the final clusters. The second part of this book will present, GraphGrepVF, an application-independent method for querying a database of graphs in order to find all the occurrences of a given subgraph. Many applications in industry, science and engineering share this problem, and increasing the size of the database requires efficient structure searching algorithms.

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.