Die vorliegende Masterarbeit dokumentiert die Erstellung eines RDF-basierten Vokabulars zur Darstellung von Nutzungsstatistiken elektronischer Ressourcen, die nach dem COUNTER-Standard erstellt wurden. Die konkrete Anwendung dieses Vokabulars bildet das Electronic Resource Management System (ERMS), welches momentan von der Universitätsbibliothek Leipzig im Rahmen des kooperativen Projektes AMSL entwickelt wird. Dieses basiert auf Linked Data, soll die veränderten Verwaltungsprozesse elektronischer Ressourcen abbilden können und gleichzeitig anbieterunabhängig und flexibel sein. Das COUNTER-Vokabular soll aber auch über diese Anwendung hinaus einsetzbar sein. Die Arbeit gliedert sich in die beiden Teile Grundlagen und Modellierung. Im ersten Teil wird zu nächst die bibliothekarische Notwendigkeit von ERM-Systemen herausgestellt und der Fokus der Betrachtung auf das Teilgebiet der Nutzungsstatistiken und die COUNTER-Standardisierung gelenkt. Anschließend werden die technischen Grundlagen der Modellierung betrachtet, um die Arbeit auch für nicht mit Linked Data vertraute Leser verständlich zu machen. Darauf folgt der Modellierungsteil, der mit einer Anforderungsanalyse sowie der Analyse des den COUNTER-Dateien zugrunde liegenden XML-Schemas beginnt. Daran schließt sich die Modellierung des Vokabulars mit Hilfe von RDFS und OWL an. Aufbauend auf angestellten Überlegungen zur Übertragung von XML-Statistiken nach RDF und der Vergabe von URIs werden anschließend reale Beispieldateien manuell konvertiert und in einem kurzen Test erfolgreich überprüft. Den Abschluss bilden ein Fazit der Arbeit sowie ein Ausblick auf das weitere Verfahren mit den Ergebnissen. Das erstellte RDF-Vokabular ist bei GitHub unter der folgenden URL zur Weiterverwendung hinterlegt: https://github.com/a-nnika/counter.vocab

Title: Exploitation of Linked Data for sharing public agreements data Author: Bc. Pavel Hryzlík Department: Department of Software Engineering Supervisor: Doc. Mgr. Martin Nečaský, Ph.D., Department of Software Engineering Abstract: The objective of the thesis is to explore the possibilities of using Linked Data principles for publishing and sharing data on contracts of public institutions and their connections to related data in the public domain (eg. Business and trade register, register of contracts, etc.). Thesis presents the entire process of opening up contracts. Defines a data standard for open contracts and proposes an ontology for the publication of data on contracts and their interconnections. Furthermore, it designs and implements a platform for publishing contracts. The first part of the platform is a conversion module enabling the conversion of contracts stored in relational databases into RDF form. Employed are R2RML mapping techniques. The second part is a uniform repository that downloads data on contracts in Linked Data format. The third part is a web application that will make the data on contracts available to end users. Keywords: Contract, Open Data, Linked Data, RDF, JSON-LD, R2RML, SPARQL Powered by TCPDF (www.tcpdf.org)

Resource Description Framework (RDF) provides a simple way for expressing facts across the web, leading to Web linked data. Several distributed and federated RDF systems have emerged to handle the massive amounts of RDF data available nowadays. Distributed systems are optimized to query massive datasets that appear as a single graph, while federated systems are designed to query hundreds of decentralized and interlinked graphs. This thesis starts with a comprehensive experimental study of the state-of-the-art RDF systems. It identifies a set of research problems for improving the state-of-the-art, including: supporting the emerging RDF analytics required by many modern applications, querying linked data at scale, and enabling discovery on linked data. Addressing these problems is the focus of this thesis. First, we propose Spartex; a versatile framework for complex RDF analytics. Spartex extends SPARQL to seamlessly combine generic graph algorithms with SPARQL queries. Spartex implements a generic SPARQL operator as a vertex-centric program that interprets SPARQL queries and executes them efficiently using a built-in optimizer. We demonstrate that Spartex scales to datasets with billions of edges, and is at least as fast as the state-of-the-art specialized RDF engines. For analytical tasks, Spartex is an order of magnitude faster than existing alternatives. To address the scalability limitation of federated RDF engines, we propose Lusail; a scalable system for querying geo-distributed RDF graphs. Lusail follows a two-tier strategy: (i) locality-aware decomposition of the query into subqueries to maximize the computations at the endpoints and minimize intermediary results, and (ii) selectivity-aware execution to reduce network latency and increase parallelism. Our experiments on billions of triples show that Lusail outperforms existing systems by orders of magnitude in scalability and response time. Finally, enabling discovery on linked data is challenging due to the prior knowledge required to formulate SPARQL queries. To address these challenges; we develop novel techniques to (i) predict semantically equivalent SPARQL queries from a set of keywords by leveraging word embeddings, and (ii) generate fine-grained and non-blocking query plans to get fast and early results.

The interplay between learning and technology is a growing field that is often referred to as Technology Enhanced Learning (TEL). Within this context, learning applications are software components that are useful for learning purposes, such as textbook replacements, information gathering tools, communication and collaboration tools, knowledge modeling tools, rich lab environments that allows experiments etc. When developing learning applications, the choice of technology depends on many factors. For instance, who and how many the intended end-users are, if there are requirements to support in-application collaboration, platform restrictions, the expertise of the developers, requirements to inter-operate with other systems or applications etc. This thesis provides guidance on a how to develop learning applications based on Semantic Web technology. The focus on Semantic Web technology is due to its basic design that allows expression of knowledge at the web scale. It also allows keeping track of who said what, providing subjective expressions in parallel with more authoritative knowledge sources. The intended readers of this thesis include practitioners such as software architects and developers as well as researchers in TEL and other related fields. The empirical part of the this thesis is the experience from the design and development of two learning applications and two supporting frameworks. The first learning application is the web application Confolio/EntryScape which allows users to collect files and online material into personal and shared portfolios. The second learning application is the desktop application Conzilla, which provides a way to create and navigate a landscape of interconnected concepts. Based upon the experience of design and development as well as on more theoretical considerations outlined in this thesis, three major obstacles have been identified: The first obstacle is: lack of non-expert and user friendly solutions for presenting and editing Semantic Web data that is not hard-coded to use a specific vocabulary. The thesis presents five categories of tools that support editing and presentation of RDF. The thesis also discusses a concrete software solution together with a list of the most important features that have crystallized during six major iterations of development. The second obstacle is: lack of solutions that can handle both private and collaborative management of resources together with related Semantic Web data. The thesis presents five requirements for a reusable read/write RDF framework and a concrete software solution that fulfills these requirements. A list of features that have appeared during four major iterations of development is also presented. The third obstacle is: lack of recommendations for how to build learning applications based on Semantic Web technology. The thesis presents seven recommendations in terms of architectures, technologies, frameworks, and type of application to focus on. In addition, as part of the preparatory work to overcome the three obstacles, the thesis also presents a categorization of applications and a derivation of the relations between standards, technologies and application types. / <p>QC 20121105</p>

Diamond is a SPARQL query engine for linked data. Linked data is a sub-topic of the Semantic Web where data is represented as a labeled directed graph using the Resource Description Framework (RDF), a conceptual data model for web resources, to affect a web-wide interconnected, distributed labeled graph. SPARQL graph patterns entail portions of this distributed graph. Diamond compiles SPARQL queries into a physical query plan based on a set of newly defined operators that implement a new variant of the Rete match, a well known artificial intelligence (AI) algorithm used for complex pattern-matching problems. / text

Hydrogeologists collect data through studies that originate from a diverse and growing set of instruments that measure, for example, geochemical constituents of surface and groundwater. Databases store and publish the collected data on the Web, and the volume of data is quickly increasing, which makes accessing data problematic and time consuming for individuals. One way to overcome this problem is to develop ontology to formally and explicitly represent the domain (e.g., contaminant hydrogeology) knowledge. Using OWL and RDF, contaminant hydrogeology ontology (CHO) is developed to manage hydrological spatial data for Georgia, USA. CHO is a conceptual computer model for the contaminant hydrogeology domain in which concepts (e.g. contaminant, aquifer) and their relationships (e.g. pollutes) are formerly and explicitly defined. Cyberinfrastructure for exposing CHO and datasets (i.e., CHKb) as Linked Data on the Web is developed. Cyberinfrastructure consists of storing, managing, querying, and visualizing CHKb that can be accessed from URL: cho.gsu.edu.

Main goal of this diploma thesis is characterization of open data, standards and analyzation of adoption and utilization of open principles in the public sector of the Czech Republic. And comparison with European Union and chosen countries. Identifies technology and tools for linked data, used for deployment of highest rating of data openness. Defines geographical data, its standards and INSPIRE directive for spatial information in Europe. The goal of practical part of thesis is to analyze adoption of open principles for geographical data between Czech institutions. Focusing on what data are available, if open principles are applied and on what circumstances are data available. Foreign countries are also covered for the comparison.

TEIXEIRA NETO, L. E. Uma abordagem para publicação de visões RDF de dados relacionais. 2014. 97 f. Dissertação (Mestrado em Ciência da Computação) - Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2014. / Submitted by Daniel Eduardo Alencar da Silva (dealencar.silva@gmail.com) on 2015-01-23T19:39:51Z No. of bitstreams: 1 2014_dis_letneto.pdf: 2039098 bytes, checksum: 476ca3810a4d9341414016b0440023ba (MD5) / Approved for entry into archive by Nirlange Queiroz(nirlange@gmail.com) on 2015-06-09T14:15:58Z (GMT) No. of bitstreams: 1 2014_dis_letneto.pdf: 2039098 bytes, checksum: 476ca3810a4d9341414016b0440023ba (MD5) / Made available in DSpace on 2015-06-09T14:15:58Z (GMT). No. of bitstreams: 1 2014_dis_letneto.pdf: 2039098 bytes, checksum: 476ca3810a4d9341414016b0440023ba (MD5) Previous issue date: 2014 / The Linked Data initiative brought new opportunities for building the next generation of Web applications. However, the full potential of linked data depends on how easy it is to transform data stored in conventional, relational databases into RDF triples. Recently, the W3C RDB2RDF Working Group proposed a standard mapping language, called R2RML, to specify customized mappings between relational schemas and target RDF vocabularies. However, the generation of customized R2RML mappings is not an easy task. Thus, it is mandatory to define: (a) a solution that maps concepts from a relational schema to terms from a RDF schema; (b) a process to support the publication of relational data into RDF, and (c) a tool that implements this process. Correspondence assertions are proposed to formalize the mappings between relational schemas and RDF schemas. Views are created to publish data from a database to a new structure or schema. The definition of RDF views over relational data allows providing this data in terms of an OWL ontology structure without having to change the database schema. In this work, we propose a three-tier architecture – database, SQL views and RDF views – where the SQL views layer maps the database concepts into RDF terms. The creation of this intermediate layer facilitates the generation of R2RML mappings and prevents that changes in the data layer result in changes on R2RML mappings. Additionally, we define a three-step process to generate the RDF views of relational data. First, the user defines the schema of the relational database and the target OWL ontology. Then, he defines correspondence assertions that formally specify the relational database in terms of the target ontology. Using these assertions, an exported ontology is generated automatically. The second step produces the SQL views that perform the mapping defined by the assertions and a R2RML mapping between these views and the exported ontology. This dissertation describes a formalization of the correspondence assertions, the three-tier architecture, the publishing process steps, the algorithms needed, a tool that supports the entire process and a case study to validate the results obtained. / A iniciativa Linked Data trouxe novas oportunidades para a construção da nova geração de aplicações Web. Entretanto, a utilização das melhores práticas estabelecidas por este padrão depende de mecanismos que facilitem a transformação dos dados armazenados em bancos relacionais em triplas RDF. Recentemente, o grupo de trabalho W3C RDB2RDF propôs uma linguagem de mapeamento padrão, denominada R2RML, para especificar mapeamentos customizados entre esquemas relacionais e vocabulários RDF. No entanto, a geração de mapeamentos R2RML não é uma tarefa fácil. É imperativo, então, definir: (a) uma solução para mapear os conceitos de um esquema relacional em termos de um esquema RDF; (b) um processo que suporte a publicação dos dados relacionais no formato RDF; e (c) uma ferramenta para facilitar a aplicação deste processo. Assertivas de correspondência são propostas para formalizar mapeamentos entre esquemas relacionais e esquemas RDF. Visões são usadas para publicar dados de uma base de dados em uma nova estrutura ou esquema. A definição de visões RDF sobre dados relacionais permite que esses dados possam ser disponibilizados em uma estrutura de termos de uma ontologia OWL, sem que seja necessário alterar o esquema da base de dados. Neste trabalho, propomos uma arquitetura em três camadas – de dados, de visões SQL e de visões RDF – onde a camada de visões SQL mapeia os conceitos da camada de dados nos termos da camada de visões RDF. A criação desta camada intermediária de visões facilita a geração dos mapeamentos R2RML e evita que alterações na camada de dados impliquem em alterações destes mapeamentos. Adicionalmente, definimos um processo em três etapas para geração das visões RDF. Na primeira etapa, o usuário define o esquema do banco de dados relacional e a ontologia OWL alvo e cria assertivas de correspondência que mapeiam os conceitos do esquema relacional nos termos da ontologia alvo. A partir destas assertivas, uma ontologia exportada é gerada automaticamente. O segundo passo produz um esquema de visões SQL gerado a partir da ontologia exportada e um mapeamento R2RML do esquema de visões para a ontologia exportada, de forma automatizada. Por fim, no terceiro passo, as visões RDF são publicadas em um SPARQL endpoint. Neste trabalho são detalhados as assertivas de correspondência, a arquitetura, o processo, os algoritmos necessários, uma ferramenta que suporta o processo e um estudo de caso para validação dos resultados obtidos.

abstract: The concept of Linked Data is gaining widespread popularity and importance. The method of publishing and linking structured data on the web is called Linked Data. Emergence of Linked Data has made it possible to make sense of huge data, which is scattered all over the web, and link multiple heterogeneous sources. This leads to the challenge of maintaining the quality of Linked Data, i.e., ensuring outdated data is removed and new data is included. The focus of this thesis is devising strategies to effectively integrate data from multiple sources, publish it as Linked Data, and maintain the quality of Linked Data. The domain used in the study is online education. With so many online courses offered by Massive Open Online Courses (MOOC), it is becoming increasingly difficult for an end user to gauge which course best fits his/her needs. Users are spoilt for choices. It would be very helpful for them to make a choice if there is a single place where they can visually compare the offerings of various MOOC providers for the course they are interested in. Previous work has been done in this area through the MOOCLink project that involved integrating data from Coursera, EdX, and Udacity and generation of linked data, i.e. Resource Description Framework (RDF) triples. The research objective of this thesis is to determine a methodology by which the quality of data available through the MOOCLink application is maintained, as there are lots of new courses being constantly added and old courses being removed by data providers. This thesis presents the integration of data from various MOOC providers and algorithms for incrementally updating linked data to maintain their quality and compare it against a naïve approach in order to constantly keep the users engaged with up-to-date data. A master threshold value was determined through experiments and analysis that quantifies one algorithm being better than the other in terms of time efficiency. An evaluation of the tool shows the effectiveness of the algorithms presented in this thesis. / Dissertation/Thesis / Masters Thesis Computer Science 2016

Several institutions collect statistical data about cities, regions, and countries for various purposes. Yet, while access to high quality and recent such data is both crucial for decision makers and a means for achieving transparency to the public, all too often such collections of data remain isolated and not re-useable, let alone comparable or properly integrated. In this paper we present the Open City Data Pipeline, a focused attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and re-publish the resulting dataset in a re-useable manner as Linked Data. The main features of the Open City Data Pipeline are: (i) we integrate and cleanse data from several sources in a modular and extensible, always up-to-date fashion; (ii) we use both Machine Learning techniques and reasoning over equational background knowledge to enrich the data by imputing missing values, (iii) we assess the estimated accuracy of such imputations per indicator. Additionally, (iv) we make the integrated and enriched data, including links to external data sources, such as DBpedia, available both in a web browser interface and as machine-readable Linked Data, using standard vocabularies such as QB and PROV. Apart from providing a contribution to the growing collection of data available as Linked Data, our enrichment process for missing values also contributes a novel methodology for combining rule-based inference about equational knowledge with inferences obtained from statistical Machine Learning approaches. While most existing works about inference in Linked Data have focused on ontological reasoning in RDFS and OWL, we believe that these complementary methods and particularly their combination could be fruitfully applied also in many other domains for integrating Statistical Linked Data, independent from our concrete use case of integrating city data.