ProDataMarket: A data marketplace for monetizing linked data

Linked data has emerged as an interesting technology for Publishing structured data on the Web but also as a powerful mechanism for integrating disparate data sources. Various tools and approaches have been developed in the semantic Web community to produce and consume linked data, however little attention has been paid to monetization of linked data. In this paper we introduce a data marketplace – proDataMarket – that enables data providers to generate, advertise, and sell linked data, and data consumers to purchase linked data on the marketplace. The marketplace was originally designed with a focus on geospatial linked data (targeting property-related data providers and consumers) but its capabilities are generic and can be used for data in various domains. This demo will highlight the capabilities offered to the providers and consumers of the data made available on the marketplace.publishedVersio

The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.publishedVersio

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.publishedVersio

ETL4LOD+: evolução do suporte ao ciclo de publicação de dados conectados

Desenvolvimento do ETL4LOD, resultado de projeto do grupo GRECO junto à RNP (Rede Nacional de Pesquisa), foi um passo importante na criação de novas soluções para limpeza e triplificação de dados no contexto de dados abertos conectados (Linked Open Data) devido à sua facilidade de uso e de extensão para novas funcionalidades. Por diversas razões, como a falta de atualizações necessárias, o interesse no ETL4LOD foi diminuindo ao decorrer dos anos. Algumas ferramentas, como Karma, Open Refine e Any23, foram testadas como possíveis alternativas porém sem sucesso. Para preencher essa lacuna que o ETL4LOD deixou nas soluções de limpeza e triplificação, este trabalho desenvolveu o ETL4LOD+. O ETL4LOD+ é uma framework filha do ETL4LOD que estende as suas funcionalidades – permitindo buscar ontologias online e o trabalho com ontologias em arquivos, e adicionando a etapa de ligação automática – além de modernizar a sua estrutura base, que seria o seu conjunto de plug-ins, código e documentação

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Tabular Data Cleaning and Linked Data Generation with Grafterizer

The volume of data being published on the Web and made available as Open Data has significantly increased over the last several years. However, data published by independent publishers are sliced and fragmented. Creating descriptive connections across datasets may considerably enrich data and extend their value. One way to standardize, describe and interconnect the information from heterogeneous data sources is to use Linked Data as a publishing technology. The majority of published open datasets is in a tabular format and the process of generating valid Linked Data from them requires powerful and flexible methods for data cleaning, preparation, and transformation. Most of the time and effort of data workers and data developers is concentrated on data cleaning aspects. In spite of the number of available platforms for tabular data cleaning and preparation, no solution is focused on the Linked Data generation. This thesis explores approaches for data cleaning and transformation in the context of the Linked Data generation and identifies their challenges. This includes reviewing typical tabular data quality issues found in the literature and practical use cases and their categorization in order to produce the requirements on designing a solution in the form of the set of data cleaning and transformation operations. Furthermore, the thesis introduces the Grafterizer software framework, developed to assist data workers and data developers in preparing and converting raw tabular data to Linked Data with simplifying and partially automating this process. The Grafterizer framework is evaluated against existing relevant tools and systems for data cleaning. The contribution of the thesis also includes extending and evaluating reference software system to implement the needed data cleaning and transformation operations. This resulted in a powerful framework for addressing typical data quality issues and a wide range of supported data cleaning and transformation operations