Title: Towards Trustworthy Linked Data Integration and Consumption Author: RNDr. Tomáš Knap Department: Department of Software Engineering Supervisor: RNDr. Irena Holubová, PhD., Department of Software Engineering Abstract: We are now finally at a point when datasets based upon open standards are being published on an increasing basis by a variety of Web communities, governmental initiatives, and various companies. Linked Data offers information consumers a level of information integration and aggregation agility that has up to now not been possible. Consumers can now "mashup" and readily integrate information for use in a myriad of alternative end uses. Indiscriminate addition of information can, however, come with inherent problems, such as the provision of poor quality, inaccurate, irrelevant or fraudulent information. All will come with associated costs of the consumed data which will negatively affect data consumer's benefit and Linked Data applications usage and uptake. In this thesis, we address these issues by proposing ODCleanStore, a Linked Da- ta management and querying tool able to provide data consumers with Linked Data, which is cleansed, properly linked, integrated, and trustworthy accord- ing to consumer's subjective requirements. Trustworthiness of data means that the data has associated...

The emergence of Linked Data in the form of knowledge graphs in RDF has been one of the most recent evolutions of the Semantic Web. This led to the development of question answering systems based on RDF and SPARQL to allow end users to access and benefit from these knowledge graphs. However, a lot of information on the Web is still unstructured, which restricts the ability of answering questions whose answer does not exist in a knowledge base. To tackle this issue, hybrid question answering has emerged as an important challenge. In fact, hybrid question answering entails the task of question answering by combining both structured (RDF) and unstructured knowledge sources (text) into one answer. This thesis tackles hybrid question answering based on natural language questions. It focuses on the analysis and improvement of an open source system called HAWK, identifies its limitations and provides solutions and recommendations in the form of a generic question-answering pipeline called HAWK_R. Our system mostly uses heuristic methods, patterns and the ontological schema and knowledge base and provides three main additions: question classification, annotation and answer verification and ranking based on query content. Our results show a clear improvement over the original HAWK based on several Question Answering over Linked Data (QALD) competitions. In fact, our methods are not limited to HAWK and can also help increase the performance of other question answering systems.

The publication and interchange of RDF datasets online has experienced significant growth in recent years, promoted by different but complementary efforts, such as Linked Open Data, the Web of Things and RDF stream processing systems. However, the current Linked Data infrastructure does not cater for the storage and exchange of sensitive or private data. On the one hand, data publishers need means to limit access to confidential data (e.g. health, financial, personal, or other sensitive data). On the other hand, the infrastructure needs to compress RDF graphs in a manner that minimises the amount of data that is both stored and transferred over the wire. In this paper, we demonstrate how HDT - a compressed serialization format for RDF - can be extended to cater for supporting encryption. We propose a number of different graph partitioning strategies and discuss the benefits and tradeoffs of each approach.

Title: Framework for geolocation-based Android applications using Linked Data Author: Bc. Matej Snoha The aim of this thesis is to design and implement a framework for geolo- cation based mobile applications using Linked Data. Introduced are Linked Data technologies in the context of mobile application develop- ment, data modeling, and geographical queries. This work follows the software development lifecycle from requirement gathering, software analysis, design of the application framework and its individual compo- nents, up to the implementation of required functionality and subsequent deployment and evaluation of the functional application framework. The resulting implementation of the framework consists of a mobile applica- tion that displays nearby places from Linked Data datasets on a map and a cloud service with a repository of required definitions. It serves to demonstrate functionality of the theoretical part of the work in real-life scenarios.

Linked data are usually visualized as graph structures which are useful for browsing resources but less useful for viewing structured data. This thesis proposes a solution how to visualize linked data in a tabular structure. The goal of the visualization is to make an orientation in data easier for a user. A tabular structure for specific data can be designed and managed in a graphic editor. Visualized data can be checked for errors and can be edited to obtain a script which fixes them. Powered by TCPDF (www.tcpdf.org)

It is recognised that nowadays, users interact with large amounts of data that exist in disparate forms, and are stored under different settings. Moreover, it is true that the amount of structured and un-structured data outside a single well organised data management system is expanding rapidly. To address the recent challenges of managing large amounts of potentially distributed data, the vision of a dataspace was introduced. This data management paradigm aims at reducing the complexity behind the challenges of integrating heterogeneous data sources. Recently, efforts by the Linked Data (LD) community gave rise to a Web of Data (WoD) that interweaves with the current Web of documents in a way that it is useful for data consumption by both humans and computational agents. On the WoD, datasets are structured under a common data model and published as Web resources following a simple set of guidelines that enables them to be linked with other pieces of data, as well as, to be annotated with useful meta data that help determine their semantics. The WoD is an evolving open ecosystem including specialist publishers as well as community efforts aiming at re-publishing isolated databases as LD on the WoD, and annotating them with meta data. The WoD raises new opportunities and challenges. However, currently it mostly relies on manual efforts for integrating the large amounts of heterogeneous data sources on the WoD. This dissertation makes the case that several techniques from the dataspaces research area (aiming at on-demand integration of data sources in a pay-as-you-go fashion) can support the integration of heterogeneous WoD sources. In so doing, this dissertation explores the opportunities and identifies the challenges of adapting existing pay-as-you-go data integration techniques in the context of LD. More specifically, this dissertation makes the following contributions: (1) a case-study for identifying the challenges when existing pay-as-you-go data integration techniques are applied in a setting where data sources are LD; (2) a methodology that deals with the 'schema-less' nature of LD sources by automatically inferring a conceptual structure from a given RDF graph thus enabling downstream tasks, such as the identification of matches and the derivation of mappings, which are, both, essential for the automatic bootstrapping of a dataspace; and (3) a well-defined, principled methodology that builds on a Bayesian inference technique for reasoning under uncertainty to improve pay-as-you-go integration. Although the developed methodology is generic in being able to reason with different hypothesis, its effectiveness has only been explored on reducing the uncertain decisions made by string-based matchers during the matching stage of a dataspace system.

The principle of Open Data and Linked data is in growing interest of many organizations, developers and even government institutions. This work is aimed on providing actual information about development of Open and Linked data, further there are introduced featured tools for creating, manipulating, transformation and other operations regarding Open and Linked Data. Finally, there is description of development of Linked Data application based on universal visualization system Payola.

The diploma thesis introduces the domain of proactive disclosure of public sector information via linked open data. At the start, the legal framework encompassing public sector information is expounded along with the basic approaches for its disclosure. The practices of publishing data as open data are defined as an ap- proach for proactive disclosure that is based on the application of the principle of openness to data with the goal to enable equal access and equal use of the data. The reviewed practices range from necessary legal actions, choices of appropriate technologies, and ways in which the technologies should be used to achieve the best data quality. Linked data is presented as a knowledge technology that, for the most part, fulfils the requirements on open technology suitable for open data. The thesis extrapolates further from the adoption of linked open data in the public sector to recognize the impact and challenges proceeding from this change. The distinctive focus on the side supplying data and the trust in the transformative effects of technological changes are identified among the key sources of these challenges. The emphasis on technologies for data disclosure at the expense of a more careful attention to the use of data is presented as a possible source of risks that may undermine the...

No / It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.

[pt] O objetivo desta dissertação é melhorar a busca por palavra-chave em formato RDF. Propomos uma abordagem escalável, baseada numa representação tensorial, que permite o armazenamento distribuído e, como consequência, o uso de técnicas de paralelismo para agilizar a busca sobre grandes bases de RDF, em particular, as publicadas como Linked Data. Um volume sem precedentes de informação está sendo disponibilizado seguindo os princípios de Linked Data, formando o que chamamos de Web of Data. Esta informação, tipicamente codificada como triplas RDF, costuma ser representada como um grafo, onde sujeitos e objetos são vértices, e predicados são arestas ligando os vértices. Em consequência da ampla adoção de mecanismos de busca na World Wide Web, usuários estão familiarizados com a busca por palavra-chave. No caso de grafos RDF, no entanto, a extração de uma partição coerente de grafos para enriquecer os resultados da busca é uma tarefa cara, demorada, e cuja expectativa do usuário é de que seja executada em tempo real. Este trabalho tem como objetivo o tratamento deste problema. Parte de uma solução proposta recentemente prega a indexação do grafo RDF como uma matriz esparsa, que contém um conjunto de informações pré-computadas para agilizar a extração de seções do grafo, e o uso de consultas baseadas em tensores sobre a matriz esparsa. Esta abordagem baseada em tensores permite que se tome vantagem de técnicas modernas de programação distribuída, e.g., a utilização de bases de dados não-relacionais fracionadas e o modelo de MapReduce. Nesta dissertação, propomos o desenho e exploramos a viabilidade da abordagem baseada em tensores, com o objetivo de construir um depósito de dados distribuído e agilizar a busca por palavras-chave com uma abordagem paralela. / [en] The goal of this dissertation is to improve RDF keyword search. We propose a scalable approach, based on a tensor representation that allows for distributed storage, and thus the use of parallel techniques to speed up the search over large linked data sets, in particular those published as Linked Data. An unprecedented amount of information is becoming available following the principles of Linked Data, forming what is called the Web of Data. This information, typically codified as RDF subject-predicate-object triples, is commonly abstracted as a graph which subjects and objects are nodes, and predicates are edges connecting them. As a consequence of the widespread adoption of search engines on the World Wide Web, users are familiar with keyword search. For RDF graphs, however, extracting a coherent subset of data graphs to enrich search results is a time consuming and expensive task, and it is expected to be executed on-the-fly at user prompt. The dissertation s goal is to handle this problem. A recent proposal has been made to index RDF graphs as a sparse matrix with the pre-computed information necessary for faster retrieval of sub-graphs, and the use of tensor-based queries over the sparse matrix. The tensor approach can leverage modern distributed computing techniques, e.g., nonrelational database sharding and the MapReduce model. In this dissertation, we propose a design and explore the viability of the tensor-based approach to build a distributed datastore and speed up keyword search with a parallel approach.