De nos jours, des masses de données sont publiées à grande échelle dans des formats numériques. Une part importante de ces données a une structure complexe, typiquement organisée sous la forme d'arbres (les documents du web, comme HTML et XML, étant les plus représentatifs) ou de graphes (en particulier, les bases de données du Web Sémantique structurées en graphes, et exprimées en RDF). Exploiter ces données complexes, qu'elles soient dans un format d'accès Open Data ou bien propriétaire (au sein d'une compagnie), présente un grand intérêt. Le faire de façon efficace pour de grands volumes de données reste encore un défi. Les vues matérialisées sont utilisées depuis longtemps pour améliorer considérablement l'évaluation des requêtes. Le principe est q'une vue stocke des résultats pre-calculés qui peuvent être utilisés pour évaluer (une partie d') une requête. L'adoption des techniques de vues matérialisées dans le contexte de données du web que nous considérons est particulièrement exigeante à cause de la complexité structurelle et sémantique des données. Cette thèse aborde deux problèmes liés à la gestion des données du web basée sur des vues matérialisées. D'abord, nous nous concentrons sur le problème de sélection des vues pour des ensembles de requêtes RDF. Nous présentons un algorithme original qui, basé sur un ensemble de requêtes, propose les vues les plus appropriées à matérialiser dans la base des données. Ceci dans le but de minimiser à la fois les coûts d'évaluation des requêtes, de maintenance et de stockage des vues. Bien que les requêtes RDF contiennent typiquement un grand nombre de jointures, ce qui complique le processus de sélection de vues, notre algorithme passe à l'échelle de centaines de requêtes, un nombre non atteint par les méthodes existantes. En outre, nous proposons des techniques nouvelles pour tenir compte des données implicites qui peuvent être dérivées des schémas RDF sans complexifier davantage la sélection des vues. La deuxième contribution de notre travail concerne la réécriture de requêtes en utilisant des vues matérialisées XML. Nous commençons par identifier un dialecte expressif de XQuery, correspondant aux motifs d'arbres avec des jointures sur la valeur, et nous étudions des propriétés importantes de ces requêtes, y compris l'inclusion et la minimisation. En nous fondant sur ces notions, nous considérons le problème de trouver des réécritures minimales et équivalentes d'une requête exprimée dans ce dialecte, en utilisant des vues matérialisées exprimées dans le même dialecte, et nous fournissons un algorithme correct et complet à cet effet. Notre travail dépasse l'état de l'art en permettant à chaque motif d'arbre de renvoyer un ensemble d'attributs, en prenant en charge des jointures sur la valeur entre les motifs, et en considérant des réécritures qui combinent plusieurs vues. Enfin, nous montrons comment notre méthode de réécriture peut être appliquée dans un contexte distribué, pour la dissémination efficace d'un corpus de documents XML annotés en RDF. / Data is being published in digital formats at very high rates nowadays. A large share of this data has complex structure, typically organized as trees (Web documents such as HTML and XML being the most representative) or graphs (in particular, graph-structured Semantic Web databases, expressed in RDF). There is great interest in exploiting such complex data, whether in an Open Data access model or within companies owning it, and efficiently doing so for large data volumes remains challenging. Materialized views have long been used to obtain significant performance improvements when processing queries. The principle is that a view stores pre-computed results that can be used to evaluate (possibly part of) a query. Adapting materialized view techniques to the Web data setting we consider is particularly challenging due to the structural and semantic complexity of the data. This thesis tackles two problems in the broad context of materialized view-based management of Web data. First, we focus on the problem of view selection for RDF query workloads. We present a novel algorithm, which, based on a query workload, proposes the most appropriate views to be materialized in the database, in order to minimize the combined cost of query evaluation, view maintenance and view storage. Although RDF query workloads typically feature many joins, hampering the view selection process, our algorithm scales to hundreds of queries, a number unattained by existing approaches. Furthermore, we propose new techniques to account for the implicit data that can be derived by the RDF Schemas and which further complicate the view selection process. The second contribution of our work concerns query rewriting based on materialized XML views. We start by identifying an expressive dialect of XQuery, corresponding to tree patterns with value joins, and study some important properties for these queries, such as containment and minimization. Based on these notions, we consider the problem of finding minimal equivalent rewritings of a query expressed in this dialect, using materialized views expressed in the same dialect, and provide a sound and complete algorithm for that purpose. Our work extends the state of the art by allowing each pattern node to return a set of attributes, supporting value joins in the patterns, and considering rewritings which combine many views. Finally, we show how our view-based query rewriting algorithm can be applied in a distributed setting, in order to efficiently disseminate corpora of XML documents carrying RDF annotations.

Made available in DSpace on 2015-04-11T14:02:41Z (GMT). No. of bitstreams: 1 andre.pdf: 821975 bytes, checksum: 8b72d2493d068d6a827082e5eb108bf6 (MD5) Previous issue date: 2011-07-28 / There is a huge amount of information in the World Wide Web in pages composed by similar objects. E-commerce Web sites and on-line catalogs, in general, are examples of such data repositories. Although this information usually occurs in semi-structured texts, it is designed to be interpreted and used by humans and not processed by machines. The identification of these objects inWeb pages is performed by external applications called extractors or wrappers. In this work we propose and evaluate an automatic approach to the problem of generating wrappers capable of extracting and structuring data records and the values of their attributes. It uses the Tree Alignment Algorithm to find in the Web page examples of objects of interest. Then, our method generates regular expressions for extracting objects similar to the examples given using the Multiple Sequence Alignment Algorithm. In a final step, the method decomposes the objects in sequences of text using the regular expression and common formats and delimiters, in order to identify the value of the attributes of the data records. Experiments using a collection composed by 128 Web pages from different domains have demonstrated the feasibility of our extraction method. It is evaluated regarding the identification of blocks of HTML source code that contain data records and regarding record extraction and the value of its attributes. It reached a precision of 83% and a recall of 80% when extracting the value of attributes. These values mean a gain in precision of 43.37% and in recall of 68.75% when compared to similar proposals. / Existe uma grande quantidade de informação na World Wide Web em páginas compostas por objetos similares. Web sites de comércio eletrônico e catálogos online, em geral, são exemplos destes repositórios de dados. Apesar destes dados serem apresentados em porções de texto semi-estruturados, são projetados para serem interpretados e utilizados por humanos e não processados por máquinas. A identificação destes objetos em páginas Web é feita por aplicações externas chamadas extratores ou wrappers. Neste trabalho propomos e avaliamos um método automático para o problema de extrair e estruturar registros e valores de seus atributos presentes em páginas Web ricas em dados. O método utiliza um Algoritmo de Alinhamento de Árvores para encontrar nestas páginas exemplos de registros que correspondem a objetos de interesse. Em seguida, o método gera expressões regulares para extrair objetos similares aos exemplos dados usando o Algoritmo de Alinhamento de Múltiplas Sequências. Em um passo final, o método decompõe os registros em sequências de texto aplicando a expressão regular criada e formatações e delimitadores comuns, com o intuito de identificar os valores dos atributos dos registros. Experimentos utilizando uma coleção composta por 128 páginasWeb de diferentes domínios demonstram a viabilidade do nosso método de extração. O método foi avaliado em relação à identificação de blocos de código HTML que contêm os registros e quanto à extração dos registros e dos valores de seus atributos. Obtivemos precisão de 83% e revocação de 80% na extração de valores de atributos. Estes valores significam um ganho na precisão de 43,37% e na revocação de 68,75%, em relação a propostas similares

Notre société est de plus en plus présente sur le Web. En conséquence, une grande partie des événements quotidiens a vocation à être numérisée. Dans ce cadre, le Web contient des descriptions de divers événements du monde réel et provenant du monde entier. L'ampleur de ces événements peut varier, allant de ceux pertinents uniquement localement à ceux qui retiennent l'attention du monde entier. La presse et les médias sociaux permettent d’atteindre une diffusion presque mondiale. L’ensemble de toutes ces données décrivant des événements sociétaux potentiellement complexes ouvre la porte à de nombreuses possibilités de recherche pour analyser et mieux comprendre l'état de notre société.Dans cette thèse, nous étudions diverses tâches d’analyse de l’impact des événements sociétaux. Plus précisément, nous abordons trois facettes dans le contexte des événements et du Web, à savoir la diffusion d’événements dans des communautés de langues étrangères, la classification automatisée des contenus Web et l’évaluation et la visualisation de la viralité de l’actualité. Nous émettons l'hypothèse que les entités nommées associées à un événement ou à un contenu Web contiennent des informations sémantiques précieuses, qui peuvent être exploitées pour créer des modèles de prédiction précis. À l'aide de nombreuses études, nous avons montré que l'élévation du contenu Web au niveau des entités saisissait leur essence essentielle et offrait ainsi une variété d'avantages pour obtenir de meilleures performances dans diverses tâches. Nous exposons de nouvelles découvertes sur des tâches disparates afin de réaliser notre objectif global en matière d'analyse de l’impact des événements sociétaux. / Our society has been rapidly growing its presence on the Web, as a consequence we are digitizing a large collection of our daily happenings. In this scenario, the Web receives virtual occurrences of various events corresponding to their real world occurrences from all around the world. Scale of these events can vary from locally relevant ones up to those that receive global attention. News and social media of current times provide all essential means to reach almost a global diffusion. This big data of complex societal events provide a platform to many research opportunities for analyzing and gaining insights into the state of our society.In this thesis, we investigate a variety of social event impact analytics tasks. Specifically, we address three facets in the context of events and the Web, namely, diffusion of events in foreign languages communities, automated classification of Web contents, and news virality assessment and visualization. We hypothesize that the named entities associated with an event or a Web content carry valuable semantic information, which can be exploited to build accurate prediction models. We have shown with the help of multiple studies that raising Web contents to the entity-level captures their core essence, and thus, provides a variety of benefits in achieving better performance in diverse tasks. We report novel findings over disparate tasks in an attempt to fulfill our overall goal on societal event impact analytics.

This report goes through the process of developing a generic article scraper which shall extract relevant information from an arbitrary web article. The extraction is implemented by searching and examining the HTML of the article, by using Python and XPath. The data that shall be extracted is the title, summary, publishing date and body text of the article. As there is no standard way that websites, and in particular news articles, is built, the extraction needs to be adapted for every different structure and language of articles. The resulting program should provide a proof of concept method of extracting the data showing that future development is possible. The thesis host company Acuminor is working with financial crime intelligence and are collecting information through articles and reports. To scale up the data collection and minimize the maintenance of the scraping programs, a general article scraper is needed. There exist an open source alternative called Newspaper, but since this is no longer being maintained and it can be argued is not properly designed, an internal implementation for the company could be beneficial. The program consists of a main class that imports extractor classes that have an API for extracting the data. Each extractor are decoupled from the rest in order to keep the program as modular as possible. The extraction for title, summary and date are similar, with the extractors looking for specific HTML tags that contain some common attribute that most websites implement. The text extraction is implemented using a tree that is built up from the existing text on the page and then searching the tree for the most likely node containing only the body text, using attributes such as amount of text, depth and number of text nodes. The resulting program does not match the performance of Newspaper, but shows promising results on every part of the extraction. The text extraction is very slow and often takes too much text of the article but provides a great blueprint for further improvement at the company. Acuminor will be able to have their in-house article extraction that suits their wants and needs. / Den här rapporten går igenom processen av att utveckla en generisk artikelskrapare som ska extrahera reöevamt information från en godtycklig artikelhemsida. Extraheringen kommer bli implementerad genom att söka igenom och undersöka HTML-en i artikeln, genom att använda Python och XPath. Datan som skall extraheras är titeln, summering, publiceringsdatum och brödtexten i artikeln. Eftersom det inte finns något standard sätt som hemsidor, och mer specifikt nyhetsartiklar är uppbyggda, extraheringen måste anpassas för varje olika struktur och språk av artiklar. Det resulterande programmed skall visa på ett bevis för ett koncept sätt att extrahera datan som visar på att framtida utveckling är möjlig. Projektets värdföretag Acuminor jobbar inom finansiell brottsintelligens och samlar ihop information genom artiklar och rapporter. För att skala upp insamlingen av data och minimera underhåll av skrapningsprogrammen, behövs en generell artikelskrapare. Det existerar ett öppen källkodsalternativ kallad Newspaper, men eftersom denna inte länge är underhållen och det kan argumenteras att den inte är så bra designad, är en intern implementation för företaget fördelaktigt. Programmet består av en huvudklass som importerar extraheringsklasser som har ett API för att extrahera datan. Varje extraherare är bortkopplad från resten av programmet för att hålla programmet så moodulärt som möjligt. Extraheringen för titel, summering och datum är liknande, där extragherarna tittar efter specifika HTML taggar som innehåller något gemensamt attribut som de flesta hemsidor implementerar. Textextraheringen är implementerad med ett träd som byggs upp från grunden från den existerande texten på sidan och sen söks igenom för att hitta den mest troliga noden som innehåller brödtexten, där den använder attribut såsom text, djup och antal textnoder. Det resulterande programmet matchar inte prestandan av Newspaper, men visar på lovande resultat vid varje del av extraheringen. Textextraheringen är väldigt långsam och hämtar ofta för mycket text från artikeln men lämnar ett bra underlag för vidare förbättring hos företaget. Allt som allt kommer Acuminor kunna bygga vidare på deras egna artikel extraherare som passar deras behov.

Le développement récent des offres commerciales autour du cloud computing a fortement influé sur la recherche et le développement des plateformes de distribution numérique. Les fournisseurs du cloud offrent une infrastructure de distribution extensible qui peut être utilisée pour le stockage et le traitement des données.En parallèle avec le développement des plates-formes de cloud computing, les modèles de programmation qui parallélisent de manière transparente l'exécution des tâches gourmandes en données sur des machines standards ont suscité un intérêt considérable, à commencer par le modèle MapReduce très connu aujourd'hui puis par d'autres frameworks plus récents et complets. Puisque ces modèles sont de plus en plus utilisés pour exprimer les tâches de traitement de données analytiques, la nécessité se fait ressentir dans l'utilisation des langages de haut niveau qui facilitent la charge de l'écriture des requêtes complexes pour ces systèmes.Cette thèse porte sur des modèles et techniques d'optimisation pour le traitement efficace de grandes masses de données du Web sur des infrastructures à grande échelle. Plus particulièrement, nous étudions la performance et le coût d'exploitation des services de cloud computing pour construire des entrepôts de données Web ainsi que la parallélisation et l'optimisation des langages de requêtes conçus sur mesure selon les données déclaratives du Web.Tout d'abord, nous présentons AMADA, une architecture d'entreposage de données Web à grande échelle dans les plateformes commerciales de cloud computing. AMADA opère comme logiciel en tant que service, permettant aux utilisateurs de télécharger, stocker et interroger de grands volumes de données Web. Sachant que les utilisateurs du cloud prennent en charge les coûts monétaires directement liés à leur consommation de ressources, notre objectif n'est pas seulement la minimisation du temps d'exécution des requêtes, mais aussi la minimisation des coûts financiers associés aux traitements de données. Plus précisément, nous étudions l'applicabilité de plusieurs stratégies d'indexation de contenus et nous montrons qu'elles permettent non seulement de réduire le temps d'exécution des requêtes mais aussi, et surtout, de diminuer les coûts monétaires liés à l'exploitation de l'entrepôt basé sur le cloud.Ensuite, nous étudions la parallélisation efficace de l'exécution de requêtes complexes sur des documents XML mis en œuvre au sein de notre système PAXQuery. Nous fournissons de nouveaux algorithmes montrant comment traduire ces requêtes dans des plans exprimés par le modèle de programmation PACT (PArallelization ConTracts). Ces plans sont ensuite optimisés et exécutés en parallèle par le système Stratosphere. Nous démontrons l'efficacité et l'extensibilité de notre approche à travers des expérimentations sur des centaines de Go de données XML.Enfin, nous présentons une nouvelle approche pour l'identification et la réutilisation des sous-expressions communes qui surviennent dans les scripts Pig Latin. Notre algorithme, nommé PigReuse, agit sur les représentations algébriques des scripts Pig Latin, identifie les possibilités de fusion des sous-expressions, sélectionne les meilleurs à exécuter en fonction du coût et fusionne d'autres expressions équivalentes pour partager leurs résultats. Nous apportons plusieurs extensions à l'algorithme afin d’améliorer sa performance. Nos résultats expérimentaux démontrent l'efficacité et la rapidité de nos algorithmes basés sur la réutilisation et des stratégies d'optimisation. / The recent development of commercial cloud computing environments has strongly impacted research and development in distributed software platforms. Cloud providers offer a distributed, shared-nothing infrastructure, that may be used for data storage and processing.In parallel with the development of cloud platforms, programming models that seamlessly parallelize the execution of data-intensive tasks over large clusters of commodity machines have received significant attention, starting with the MapReduce model very well known by now, and continuing through other novel and more expressive frameworks. As these models are increasingly used to express analytical-style data processing tasks, the need for higher-level languages that ease the burden of writing complex queries for these systems arises.This thesis investigates the efficient management of Web data on large-scale infrastructures. In particular, we study the performance and cost of exploiting cloud services to build Web data warehouses, and the parallelization and optimization of query languages that are tailored towards querying Web data declaratively.First, we present AMADA, an architecture for warehousing large-scale Web data in commercial cloud platforms. AMADA operates in a Software as a Service (SaaS) approach, allowing users to upload, store, and query large volumes of Web data. Since cloud users support monetary costs directly connected to their consumption of resources, our focus is not only on query performance from an execution time perspective, but also on the monetary costs associated to this processing. In particular, we study the applicability of several content indexing strategies, and show that they lead not only to reducing query evaluation time, but also, importantly, to reducing the monetary costs associated with the exploitation of the cloud-based warehouse.Second, we consider the efficient parallelization of the execution of complex queries over XML documents, implemented within our system PAXQuery. We provide novel algorithms showing how to translate such queries into plans expressed in the PArallelization ConTracts (PACT) programming model. These plans are then optimized and executed in parallel by the Stratosphere system. We demonstrate the efficiency and scalability of our approach through experiments on hundreds of GB of XML data.Finally, we present a novel approach for identifying and reusing common subexpressions occurring in Pig Latin scripts. In particular, we lay the foundation of our reuse-based algorithms by formalizing the semantics of the Pig Latin query language with extended nested relational algebra for bags. Our algorithm, named PigReuse, operates on the algebraic representations of Pig Latin scripts, identifies subexpression merging opportunities, selects the best ones to execute based on a cost function, and merges other equivalent expressions to share its result. We bring several extensions to the algorithm to improve its performance. Our experiment results demonstrate the efficiency and effectiveness of our reuse-based algorithms and optimization strategies.

The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic analysis. Result of the work is a design and implementation of summarization module for Python language. Final part of the work contains evaluation of summaries generated by implemented summarization methods and their subjective comparison of the author.

This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have seen the emergence of numerous approaches in various disciplines concerned with linguistic resources and NLP tools. It is the challenge of our time to store, interlink and exploit this wealth of data accumulated in more than half a century of computational linguistics, of empirical, corpus-based study of language, and of computational lexicography in all its heterogeneity. The vision of the Giant Global Graph (GGG) was conceived by Tim Berners-Lee aiming at connecting all data on the Web and allowing to discover new relations between this openly-accessible data. This vision has been pursued by the Linked Open Data (LOD) community, where the cloud of published datasets comprises 295 data repositories and more than 30 billion RDF triples (as of September 2011). RDF is based on globally unique and accessible URIs and it was specifically designed to establish links between such URIs (or resources). This is captured in the Linked Data paradigm that postulates four rules: (1) Referred entities should be designated by URIs, (2) these URIs should be resolvable over HTTP, (3) data should be represented by means of standards such as RDF, (4) and a resource should include links to other resources. Although it is difficult to precisely identify the reasons for the success of the LOD effort, advocates generally argue that open licenses as well as open access are key enablers for the growth of such a network as they provide a strong incentive for collaboration and contribution by third parties. In his keynote at BNCOD 2011, Chris Bizer argued that with RDF the overall data integration effort can be “split between data publishers, third parties, and the data consumer”, a claim that can be substantiated by observing the evolution of many large data sets constituting the LOD cloud. As written in the acknowledgement section, parts of this thesis has received numerous feedback from other scientists, practitioners and industry in many different ways. The main contributions of this thesis are summarized here: Part I – Introduction and Background. During his keynote at the Language Resource and Evaluation Conference in 2012, Sören Auer stressed the decentralized, collaborative, interlinked and interoperable nature of the Web of Data. The keynote provides strong evidence that Semantic Web technologies such as Linked Data are on its way to become main stream for the representation of language resources. The jointly written companion publication for the keynote was later extended as a book chapter in The People’s Web Meets NLP and serves as the basis for “Introduction” and “Background”, outlining some stages of the Linked Data publication and refinement chain. Both chapters stress the importance of open licenses and open access as an enabler for collaboration, the ability to interlink data on the Web as a key feature of RDF as well as provide a discussion about scalability issues and decentralization. Furthermore, we elaborate on how conceptual interoperability can be achieved by (1) re-using vocabularies, (2) agile ontology development, (3) meetings to refine and adapt ontologies and (4) tool support to enrich ontologies and match schemata. Part II - Language Resources as Linked Data. “Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge Acquisition Spiral” summarize the results of the Linked Data in Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in 2013 and give a preview of the MLOD special issue. In total, five proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP & DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012) and one journal special issue (Multilingual Linked Open Data, MLOD to appear) – have been (co-)edited to create incentives for scientists to convert and publish Linked Data and thus to contribute open and/or linguistic data to the LOD cloud. Based on the disseminated call for papers, 152 authors contributed one or more accepted submissions to our venues and 120 reviewers were involved in peer-reviewing. “DBpedia as a Multilingual Language Resource” and “Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked Data Cloud” contain this thesis’ contribution to the DBpedia Project in order to further increase the size and inter-linkage of the LOD Cloud with lexical-semantic resources. Our contribution comprises extracted data from Wiktionary (an online, collaborative dictionary similar to Wikipedia) in more than four languages (now six) as well as language-specific versions of DBpedia, including a quality assessment of inter-language links between Wikipedia editions and internationalized content negotiation rules for Linked Data. In particular the work described in created the foundation for a DBpedia Internationalisation Committee with members from over 15 different languages with the common goal to push DBpedia as a free and open multilingual language resource. Part III - The NLP Interchange Format (NIF). “NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and “Evaluation and Related Work” constitute one of the main contribution of this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. The core specification is included in and describes which URI schemes and RDF vocabularies must be used for (parts of) natural language texts and annotations in order to create an RDF/OWL-based interoperability layer with NIF built upon Unicode Code Points in Normal Form C. In , classes and properties of the NIF Core Ontology are described to formally define the relations between text, substrings and their URI schemes. contains the evaluation of NIF. In a questionnaire, we asked questions to 13 developers using NIF. UIMA, GATE and Stanbol are extensible NLP frameworks and NIF was not yet able to provide off-the-shelf NLP domain ontologies for all possible domains, but only for the plugins used in this study. After inspecting the software, the developers agreed however that NIF is adequate enough to provide a generic RDF output based on NIF using literal objects for annotations. All developers were able to map the internal data structure to NIF URIs to serialize RDF output (Adequacy). The development effort in hours (ranging between 3 and 40 hours) as well as the number of code lines (ranging between 110 and 445) suggest, that the implementation of NIF wrappers is easy and fast for an average developer. Furthermore the evaluation contains a comparison to other formats and an evaluation of the available URI schemes for web annotation. In order to collect input from the wide group of stakeholders, a total of 16 presentations were given with extensive discussions and feedback, which has lead to a constant improvement of NIF from 2010 until 2013. After the release of NIF (Version 1.0) in November 2011, a total of 32 vocabulary employments and implementations for different NLP tools and converters were reported (8 by the (co-)authors, including Wiki-link corpus, 13 by people participating in our survey and 11 more, of which we have heard). Several roll-out meetings and tutorials were held (e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC 2014). Part IV - The NLP Interchange Format in Use. “Use Cases and Applications for NIF” and “Publication of Corpora using NIF” describe 8 concrete instances where NIF has been successfully used. One major contribution in is the usage of NIF as the recommended RDF mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard and the conversion algorithms from ITS to NIF and back. One outcome of the discussions in the standardization meetings and telephone conferences for ITS 2.0 resulted in the conclusion there was no alternative RDF format or vocabulary other than NIF with the required features to fulfill the working group charter. Five further uses of NIF are described for the Ontology of Linguistic Annotations (OLiA), the RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and visualisations of NIF using the RelFinder tool. These 8 instances provide an implemented proof-of-concept of the features of NIF. starts with describing the conversion and hosting of the huge Google Wikilinks corpus with 40 million annotations for 3 million web sites. The resulting RDF dump contains 477 million triples in a 5.6 GB compressed dump file in turtle syntax. describes how NIF can be used to publish extracted facts from news feeds in the RDFLiveNews tool as Linked Data. Part V - Conclusions. provides lessons learned for NIF, conclusions and an outlook on future work. Most of the contributions are already summarized above. One particular aspect worth mentioning is the increasing number of NIF-formated corpora for Named Entity Recognition (NER) that have come into existence after the publication of the main NIF paper Integrating NLP using Linked Data at ISWC 2013. These include the corpora converted by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP Interchange Format for Open German Governmental Data, N^3 – A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format and Global Intelligent Content: Active Curation of Language Resources using Linked Data as well as an early implementation of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr. Further funding for the maintenance, interlinking and publication of Linguistic Linked Data as well as support and improvements of NIF is available via the expiring LOD2 EU project, as well as the CSA EU project called LIDER, which started in November 2013. Based on the evidence of successful adoption presented in this thesis, we can expect a decent to high chance of reaching critical mass of Linked Data technology as well as the NIF standard in the field of Natural Language Processing and Language Resources.:CONTENTS i introduction and background 1 1 introduction 3 1.1 Natural Language Processing . . . . . . . . . . . . . . . 3 1.2 Open licenses, open access and collaboration . . . . . . 5 1.3 Linked Data in Linguistics . . . . . . . . . . . . . . . . . 6 1.4 NLP for and by the Semantic Web – the NLP Inter- change Format (NIF) . . . . . . . . . . . . . . . . . . . . 8 1.5 Requirements for NLP Integration . . . . . . . . . . . . 10 1.6 Overview and Contributions . . . . . . . . . . . . . . . 11 2 background 15 2.1 The Working Group on Open Data in Linguistics (OWLG) 15 2.1.1 The Open Knowledge Foundation . . . . . . . . 15 2.1.2 Goals of the Open Linguistics Working Group . 16 2.1.3 Open linguistics resources, problems and chal- lenges . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Recent activities and on-going developments . . 18 2.2 Technological Background . . . . . . . . . . . . . . . . . 18 2.3 RDF as a data model . . . . . . . . . . . . . . . . . . . . 21 2.4 Performance and scalability . . . . . . . . . . . . . . . . 22 2.5 Conceptual interoperability . . . . . . . . . . . . . . . . 22 ii language resources as linked data 25 3 linked data in linguistics 27 3.1 Lexical Resources . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Linguistic Corpora . . . . . . . . . . . . . . . . . . . . . 30 3.3 Linguistic Knowledgebases . . . . . . . . . . . . . . . . 31 3.4 Towards a Linguistic Linked Open Data Cloud . . . . . 32 3.5 State of the Linguistic Linked Open Data Cloud in 2012 33 3.6 Querying linked resources in the LLOD . . . . . . . . . 36 3.6.1 Enriching metadata repositories with linguistic features (Glottolog → OLiA) . . . . . . . . . . . 36 3.6.2 Enriching lexical-semantic resources with lin- guistic information (DBpedia (→ POWLA) → OLiA) . . . . . . . . . . . . . . . . . . . . . . . . 38 4 DBpedia as a multilingual language resource: the case of the greek dbpedia edition. 39 4.1 Current state of the internationalization effort . . . . . 40 4.2 Language-specific design of DBpedia resource identifiers 41 4.3 Inter-DBpedia linking . . . . . . . . . . . . . . . . . . . 42 4.4 Outlook on DBpedia Internationalization . . . . . . . . 44 5 leveraging the crowdsourcing of lexical resources for bootstrapping a linguistic linked data cloud 47 5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 Problem Description . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Processing Wiki Syntax . . . . . . . . . . . . . . 50 5.2.2 Wiktionary . . . . . . . . . . . . . . . . . . . . . . 52 5.2.3 Wiki-scale Data Extraction . . . . . . . . . . . . . 53 5.3 Design and Implementation . . . . . . . . . . . . . . . . 54 5.3.1 Extraction Templates . . . . . . . . . . . . . . . . 56 5.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . 56 5.3.3 Language Mapping . . . . . . . . . . . . . . . . . 58 5.3.4 Schema Mediation by Annotation with lemon . 58 5.4 Resulting Data . . . . . . . . . . . . . . . . . . . . . . . . 58 5.5 Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . 60 5.6 Discussion and Future Work . . . . . . . . . . . . . . . 60 5.6.1 Next Steps . . . . . . . . . . . . . . . . . . . . . . 61 5.6.2 Open Research Questions . . . . . . . . . . . . . 61 6 nlp & dbpedia, an upward knowledge acquisition spiral 63 6.1 Knowledge acquisition and structuring . . . . . . . . . 64 6.2 Representation of knowledge . . . . . . . . . . . . . . . 65 6.3 NLP tasks and applications . . . . . . . . . . . . . . . . 65 6.3.1 Named Entity Recognition . . . . . . . . . . . . 66 6.3.2 Relation extraction . . . . . . . . . . . . . . . . . 67 6.3.3 Question Answering over Linked Data . . . . . 67 6.4 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4.1 Gold and silver standards . . . . . . . . . . . . . 69 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iii the nlp interchange format (nif) 73 7 nif 2.0 core specification 75 7.1 Conformance checklist . . . . . . . . . . . . . . . . . . . 75 7.2 Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2.1 Definition of Strings . . . . . . . . . . . . . . . . 78 7.2.2 Representation of Document Content with the nif:Context Class . . . . . . . . . . . . . . . . . . 80 7.3 Extension of NIF . . . . . . . . . . . . . . . . . . . . . . 82 7.3.1 Part of Speech Tagging with OLiA . . . . . . . . 83 7.3.2 Named Entity Recognition with ITS 2.0, DBpe- dia and NERD . . . . . . . . . . . . . . . . . . . 84 7.3.3 lemon and Wiktionary2RDF . . . . . . . . . . . 86 8 nif 2.0 resources and architecture 89 8.1 NIF Core Ontology . . . . . . . . . . . . . . . . . . . . . 89 8.1.1 Logical Modules . . . . . . . . . . . . . . . . . . 90 8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . 91 8.2.1 Access via REST Services . . . . . . . . . . . . . 92 8.2.2 NIF Combinator Demo . . . . . . . . . . . . . . 92 8.3 Granularity Profiles . . . . . . . . . . . . . . . . . . . . . 93 8.4 Further URI Schemes for NIF . . . . . . . . . . . . . . . 95 8.4.1 Context-Hash-based URIs . . . . . . . . . . . . . 99 9 evaluation and related work 101 9.1 Questionnaire and Developers Study for NIF 1.0 . . . . 101 9.2 Qualitative Comparison with other Frameworks and Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 9.3 URI Stability Evaluation . . . . . . . . . . . . . . . . . . 103 9.4 Related URI Schemes . . . . . . . . . . . . . . . . . . . . 104 iv the nlp interchange format in use 109 10 use cases and applications for nif 111 10.1 Internationalization Tag Set 2.0 . . . . . . . . . . . . . . 111 10.1.1 ITS2NIF and NIF2ITS conversion . . . . . . . . . 112 10.2 OLiA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 10.3 RDFaCE . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 10.4 Tiger Corpus Navigator . . . . . . . . . . . . . . . . . . 121 10.4.1 Tools and Resources . . . . . . . . . . . . . . . . 122 10.4.2 NLP2RDF in 2010 . . . . . . . . . . . . . . . . . . 123 10.4.3 Linguistic Ontologies . . . . . . . . . . . . . . . . 124 10.4.4 Implementation . . . . . . . . . . . . . . . . . . . 125 10.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . 126 10.4.6 Related Work and Outlook . . . . . . . . . . . . 129 10.5 OntosFeeder – a Versatile Semantic Context Provider for Web Content Authoring . . . . . . . . . . . . . . . . 131 10.5.1 Feature Description and User Interface Walk- through . . . . . . . . . . . . . . . . . . . . . . . 132 10.5.2 Architecture . . . . . . . . . . . . . . . . . . . . . 134 10.5.3 Embedding Metadata . . . . . . . . . . . . . . . 135 10.5.4 Related Work and Summary . . . . . . . . . . . 135 10.6 RelFinder: Revealing Relationships in RDF Knowledge Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 10.6.1 Implementation . . . . . . . . . . . . . . . . . . . 137 10.6.2 Disambiguation . . . . . . . . . . . . . . . . . . . 138 10.6.3 Searching for Relationships . . . . . . . . . . . . 139 10.6.4 Graph Visualization . . . . . . . . . . . . . . . . 140 10.6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . 141 11 publication of corpora using nif 143 11.1 Wikilinks Corpus . . . . . . . . . . . . . . . . . . . . . . 143 11.1.1 Description of the corpus . . . . . . . . . . . . . 143 11.1.2 Quantitative Analysis with Google Wikilinks Cor- pus . . . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2 RDFLiveNews . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . 145 11.2.2 Mapping to RDF and Publication on the Web of Data . . . . . . . . . . . . . . . . . . . . . . . . . 146 v conclusions 149 12 lessons learned, conclusions and future work 151 12.1 Lessons Learned for NIF . . . . . . . . . . . . . . . . . . 151 12.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 151 12.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 153