Dissertations / Theses on the topic 'Semantisches Netz'

In der Arbeit wird ein Ansatz zur Verwaltung von Lernmaterialien in einem semistrukturierten Datenbanksystem vorgestellt. Der Ansatz zeichnet sich durch die Wiederverwendung einmal gespeicherter Lernmaterialien aus

Die öffentliche Verwaltung setzt seit mehreren Jahren E-Government-Anwendungssysteme ein, um ihre Verwaltungsprozesse intensiver mit moderner Informationstechnik zu unterstützen. Da die öffentliche Verwaltung in ihrem Handeln in besonderem Maße an Recht und Gesetz gebunden ist verstärkt und verbreitet sich der Zusammenhang zwischen den Gesetzen und Rechtsvorschriften einerseits und der zur Aufgabenunterstützung eingesetzten Informationstechnik andererseits. Aus Sicht der Softwaretechnik handelt es sich bei diesem Zusammenhang um eine spezielle Form der Verfolgbarkeit von Anforderungen (engl. Traceability), die so genannte Verfolgbarkeit im Vorfeld der Anforderungsspezifikation (Pre-Requirements Specification Traceability, kurz Pre-RS Traceability), da sie Aspekte betrifft, die relevant sind, bevor die Anforderungen in eine Spezifikation eingeflossen sind (Ursprünge von Anforderungen). Der Ansatz dieser Arbeit leistet einen Beitrag zur Verfolgbarkeit im Vorfeld der Anforderungsspezifikation von E-Government-Anwendungssystemen. Er kombiniert dazu aktuelle Entwicklungen und Standards (insbesondere des World Wide Web Consortium und der Object Management Group) aus den Bereichen Verfolgbarkeit von Anforderungen, Semantic Web, Ontologiesprachen und modellgetriebener Softwareentwicklung. Der Lösungsansatz umfasst eine spezielle Ontologie des Verwaltungshandeln, die mit den Techniken, Methoden und Werkzeugen des Semantic Web eingesetzt wird, um in Texten von Rechtsvorschriften relevante Ursprünge von Anforderungen durch Annotationen mit einer definierten Semantik zu versehen. Darauf aufbauend wird das Ontology Definition Metamodel (ODM) verwendet, um die Annotationen als spezielle Individuen einer Ontologie auf Elemente der Unified Modeling Language (UML) abzubilden. Dadurch entsteht ein neuer Modelltyp Pre-Requirements Model (PRM), der das Vorfeld der Anforderungsspezifikation formalisiert. Modelle diesen Typs können auch verwendet werden, um Aspekte zu formalisieren die sich nicht oder nicht vollständig aus dem Text der Rechtsvorschrift ergeben. Weiterhin bietet das Modell die Möglichkeit zum Anschluss an die modellgetriebene Softwareentwicklung. In der Arbeit wird deshalb eine Erweiterung der Model Driven Architecture (MDA) vorgeschlagen. Zusätzlich zu den etablierten Modelltypen Computation Independent Model (CIM), Platform Independent Model (PIM) und Platform Specific Model (PSM) könnte der Einsatz des PRM Vorteile für die Verfolgbarkeit bringen. Wird die MDA mit dem PRM auf das Vorfeld der Anforderungsspezifikation ausgeweitet, kann eine Transformation des PRM in ein CIM als initiale Anforderungsspezifikation erfolgen, indem der MOF Query View Transformation Standard (QVT) eingesetzt wird. Als Teil des QVT-Standards ist die Aufzeichnung von Verfolgbarkeitsinformationen bei Modelltransformationen verbindlich. Um die semantische Lücke zwischen PRM und CIM zu überbrücken, erfolgt analog zum Einsatz des Plattformmodells (PM) in der PIM nach PSM Transformation der Einsatz spezieller Hilfsmodelle. Es kommen dafür die im Projekt "E-LoGo" an der Universität Potsdam entwickelten Referenzmodelle zum Einsatz. Durch die Aufzeichnung der Abbildung annotierter Textelemente auf Elemente im PRM und der Transformation der Elemente des PRM in Elemente des CIM kann durchgängige Verfolgbarkeit im Vorfeld der Anforderungsspezifikation erreicht werden. Der Ansatz basiert auf einer so genannten Verfolgbarkeitsdokumentation in Form verlinkter Hypertextdokumente, die mittels XSL-Stylesheet erzeugt wurden und eine Verbindung zur graphischen Darstellung des Diagramms (z. B. Anwendungsfall-, Klassendiagramm der UML) haben. Der Ansatz unterstützt die horizontale Verfolgbarkeit zwischen Elementen unterschiedlicher Modelle vorwärts- und rückwärtsgerichtet umfassend. Er bietet außerdem vertikale Verfolgbarkeit, die Elemente des gleichen Modells und verschiedener Modellversionen in Beziehung setzt. Über den offensichtlichen Nutzen einer durchgängigen Verfolgbarkeit im Vorfeld der Anforderungsspezifikation (z. B. Analyse der Auswirkungen einer Gesetzesänderung, Berücksichtigung des vollständigen Kontextes einer Anforderung bei ihrer Priorisierung) hinausgehend, bietet diese Arbeit eine erste Ansatzmöglichkeit für eine Feedback-Schleife im Prozess der Gesetzgebung. Stehen beispielsweise mehrere gleichwertige Gestaltungsoptionen eines Gesetzes zur Auswahl, können die Auswirkungen jeder Option analysiert und der Aufwand ihrer Umsetzung in E-Government-Anwendungen als Auswahlkriterium berücksichtigt werden. Die am 16. März 2011 in Kraft getretene Änderung des NKRG schreibt eine solche Analyse des so genannten „Erfüllungsaufwands“ für Teilbereiche des Verwaltungshandelns bereits heute verbindlich vor. Für diese Analyse kann die vorliegende Arbeit einen Ansatz bieten, um zu fundierten Aussagen über den Änderungsaufwand eingesetzter E-Government-Anwendungssysteme zu kommen.
Public administration is using electronic government (e-government) application systems for several years to support their processes more intensive with modern information and communication technology than ever before. This increases and broadens the relationship between law and legislation executed by the administration on the one hand and requirements to e-government application systems used to support administrative execution on the other hand. This relationship is subject matter of pre-requirements specification traceability (pre-RS traceability). This work introduces an approach to pre-RS traceabiliy for e-government application. It combines research efforts and standards (i.e. of World Wide Web Consortium and Object Management Group) from different fields: traceability, semantic web, ontology engineering and model driven software engineering. Using this approach it is possible to add a semantic to elements of law and legislation texts using annotations. Annotation semantics is based on an ontology of public administration execution developed especially for this approach. A mapping from annotated text elements as a special kind of ontology individuals to elements of Unified Modeling Language (UML) is created using the Ontology Definition Metamodel (ODM). This mapping results in a new model type referred to as Pre-Requirements Model (PRM). This model uses elements that exist before requirements are explicitly documented in a requirements specification. Therefore it can be primary used to formalize elements and their relationships in the pre-requirements scope. Through the mapping rules of ODM it keeps a traceable relationship from each model element to its corresponding annotated text elements. PRM can also be used to model and refine elements that are not or not completely derived directly from text of law and legislation. In this work is argued that Model Driven Architecture (MDA) might profit from extending the existing model types Computation Independent Model (CIM), Platform Independent Model (PIM) and Platform Specific Model (PSM) by using a PRM. This extension leads to an Architecture that starts with a pre-requirements viewpoint before any requirements are formalized and documented in models of type CIM. It offers also the opportunity to use model transformation to create an initial CIM from PRM by allying the MOF Query View Transformation standard (QVT). Using QVT ensures the traceability of model transformation because standard enforces recording of traceability information. A Transformation from PRM to CIM creates an initial requirements specification that can be refined using common techniques, methods and tools. To bridge the semantic gap between PRM and CIM the approach follows the pattern of PIM to PSM transformation which uses the Platform Model (PM). Analogues PRM to CIM transformation uses special reference models for e-government developed in the project "E-LoGo" at university of Potsdam. By recoding traces of mapping annotation to elements in PRM and transforming elements of PRM to elements in CIM using reference models continuous pre-RS traceability can be achieved. The approach uses simple Extensible Stylesheet Language Transformations (XSLT) to create a hypertext documentation that links all relevant elements. Navigating along these links makes it possible for example to start with an annotated element of a law text and follow to all resulting requirements in a CIM. Using the opposite direction it is possible to see for each requirement from which text element of a law it is derived or even if there is no relation to law. By integrating the graphical representation of a model element this navigation can even start directly in a UML diagram. This illustrates that the approach offers vertical and horizontal traceability in forward and backward direction. Besides the obvious use cases continuous pre-requirements specification traceability offers in general (i.e. impact analysis on changes of law and legislation, consider context of a requirements when prioritizing them) is also offers the chance to create a feedback on the consequences of a change in law to existing e-government systems. As long as alternatives and the necessary scope in legislative process are still left, a feedback can be used to choose an alternative with less effort or faster implementation. For federal law it is in Germany since 2011 obligatory to make a similar estimation referred to as achievement effort (“Erfüllungsaufwand”). This work contributes to the first step of making a solid estimation of this kind of effort using pre-RS traceability.

Gescannte und vektorisierte technische Zeichnungen werden automatisch unter Nutzung eines Netzes von Modellen in eine hochwertige Datenstruktur migriert. Die Modelle beschreiben die Inhalte der Zeichnungen hierarchisch und deklarativ. Modelle für einzelne Bestandteile der Zeichnungen können paarweise unabhängig entwickelt werden. Dadurch werden auch sehr komplexe Zeichnungsklassen wie Elektroleitungsnetze oder Gebäudepläne zugänglich. Die Modelle verwendet der neue, sogenannte Y-Algorithmus: Hypothesen über die Deutung lokaler Zeichnungsinhalte werden hierarchisch generiert. Treten bei der Nutzung konkurrierender Modelle Konflikte auf, werden diese protokolliert. Mittels des Konfliktbegriffes können konsistente Interpretationen einer kompletten Zeichnung abstrakt definiert und während der Analyse einer konkreten Zeichnung bestimmt werden. Ein wahrscheinlichkeitsbasiertes Gütemaß bewertet jede dieser alternativen, globalen Interpretationen. Das Suchen einer bzgl. dieses Maßes optimalen Interpretation ist ein NP-hartes Problem. Ein Branch and Bound-Algorithmus stellt die adäquate Lösung dar.

Gescannte und vektorisierte technische Zeichnungen werden automatisch unter Nutzung eines Netzes von Modellen in eine hochwertige Datenstruktur migriert. Die Modelle beschreiben die Inhalte der Zeichnungen hierarchisch und deklarativ. Modelle für einzelne Bestandteile der Zeichnungen können paarweise unabhängig entwickelt werden. Dadurch werden auch sehr komplexe Zeichnungsklassen wie Elektroleitungsnetze oder Gebäudepläne zugänglich. Die Modelle verwendet der neue, sogenannte Y-Algorithmus: Hypothesen über die Deutung lokaler Zeichnungsinhalte werden hierarchisch generiert. Treten bei der Nutzung konkurrierender Modelle Konflikte auf, werden diese protokolliert. Mittels des Konfliktbegriffes können konsistente Interpretationen einer kompletten Zeichnung abstrakt definiert und während der Analyse einer konkreten Zeichnung bestimmt werden. Ein wahrscheinlichkeitsbasiertes Gütemaß bewertet jede dieser alternativen, globalen Interpretationen. Das Suchen einer bzgl. dieses Maßes optimalen Interpretation ist ein NP-hartes Problem. Ein Branch and Bound-Algorithmus stellt die adäquate Lösung dar.

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.

Ziel der Arbeit ist die Entwicklung und Prüfung eines semantischen Informationssystems zur Unterstützung des Informations- und Wissenstransfers bei kollektiver, internetbasierter Informationsarbeit – sowohl innerhalb des Teams, als auch in dessen Übertragung in das Unternehmen. Als Informationssystem wird dabei ein System verstanden, welches die menschlichen Aufgabenträger, die organisationale und technische Ausgestaltung, aber auch die zu unterstützende Aufgabe determiniert und beinhaltet und von der Systemumwelt abgrenzt. Der Fokus der Arbeit ist in den Gegenstandsbereich der Wirtschaftsinformatik einzuordnen. Die Wirtschaftsinformatik besitzt nach dem Verständnis des Autors einen transdisziplinären Charakter und der Nukleus der Fachdisziplin ist als Intermediär verschiedener beteiligter, angrenzender Disziplinen zu verstehen. So sind bei der Betrachtung der avisierten Arbeit ebenso Einflüsse der Betriebswirtschaftslehre, der Informations- und Kommunikationstechno- logie, soziologischer sowie psychologischer Aspekte und nicht zuletzt linguistischer Grundlagen von Interesse für das Resultat bzw. Einflussdimensionen des Ergebnisses.

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

Ziel der Arbeit ist die Entwicklung und Prüfung eines semantischen Informationssystems zur Unterstützung des Informations- und Wissenstransfers bei kollektiver, internetbasierter Informationsarbeit – sowohl innerhalb des Teams, als auch in dessen Übertragung in das Unternehmen. Als Informationssystem wird dabei ein System verstanden, welches die menschlichen Aufgabenträger, die organisationale und technische Ausgestaltung, aber auch die zu unterstützende Aufgabe determiniert und beinhaltet und von der Systemumwelt abgrenzt. Der Fokus der Arbeit ist in den Gegenstandsbereich der Wirtschaftsinformatik einzuordnen. Die Wirtschaftsinformatik besitzt nach dem Verständnis des Autors einen transdisziplinären Charakter und der Nukleus der Fachdisziplin ist als Intermediär verschiedener beteiligter, angrenzender Disziplinen zu verstehen. So sind bei der Betrachtung der avisierten Arbeit ebenso Einflüsse der Betriebswirtschaftslehre, der Informations- und Kommunikationstechno- logie, soziologischer sowie psychologischer Aspekte und nicht zuletzt linguistischer Grundlagen von Interesse für das Resultat bzw. Einflussdimensionen des Ergebnisses.:GRUNDLEGUNG I Problemkontext II Motivation zu dieser Arbeit III Ziel der Arbeit IV Forschungsdesign V Gang der Arbeit TEIL A: VERSTEHEN, LERNEN, WISSEN 1 Wissen und dessen Konstitution 1.1 Gegenstand des Wissens 1.2 Organisationale Epistemologie von Wissen 1.3 Einige Daten und Informationen zum Wissen 1.4 Die Natur des Wissens 1.5 Systematisierung der Arten von Wissen 1.6 Implikationen für diese Arbeit 2 Kommunikation & Verstehen 2.1 Partnerorientiertes Handeln 2.2 Kommunikation, Sprache und Textproduktion 2.3 Wahrnehmen, Verstehen & Rezipieren 2.4 Symbolische Interaktion 2.5 Barrieren der symbolischen Interaktion 2.6 Konsolidierung 3 Wissen & Lernen 3.1 Lernen in Schleifen 3.2 Theorien des individuelles Lernen 3.3 Kollektives Lernen 3.4 Die lernende Organisation 3.5 Kontinuum des Lernens 4 Wissenstransfer und die lernende Organisation 4.1 Strategien des Wissensmanagements 4.2 Wissens(re-)konstruktion 4.3 Erfolgsfaktoren für Wissenstransfer 4.4 Zyklische Wissensentstehung 4.5 Organisationale Wissensschaffung TEIL B: KOLLEKTIVE INFORMATIONSARBEIT & ORGANISATIONALES LERNEN 5 Informationsarbeit & Wissensschaffung 5.1 Vom Suchen und Finden von Informationen 5.2 Information Literacy & Recherchekompetenz 5.3 Informationsarbeit 5.4 Recherchestrategien 5.5 Phasen der internetbasierten Recherche 6 Organisation kollektiver Informationsarbeit 6.1 Intensität von Zusammenarbeit 6.2 Formale & informelle Arbeitsgruppen 6.3 Informationsarbeit im Team 6.4 Die Aufgabe als konstituierendes Merkmal 6.5 Individuelle Dispositionen 6.6 Gruppendynamik im Projektteam 6.7 Externe Unterstützung des Teams 6.8 Soziotechnischer Rahmen 6.9 Kollektive, internetbasierte Informationsarbeit 7 Die suchende Hypertextorganisation 7.1 Die organisationale Wissensbasis 7.2 Soziales Kapital & soziale Netzwerke 7.3 Wissen aus kollektiver Informationsarbeit 7.4 Kollektive Informationsarbeit und die lernende Organisation 7.5 Die lernende Organisation als Community of Communitys TEIL C: AUTOMATISIERTE KONTEXTMODELLIERUNG KOLLEKTIVER INFORMATIONSARBEIT 8 Konzeptuelle & technische Prämissen 8.1 Kernfunktionen der kollektiven Rechercheumgebung 8.2 Multidimensionalität vs. Multiperspektivität 8.3 Das Kontinuum semantischer Strukturmodelle 8.4 Konzepte der technischen Realisierung 9 Kontextschaffung bei kollektiver Informationsarbeit 9.1 Rechercheprozess und formale Objekte 9.2 Kollektive Terminologiearbeit und formale Rechercheobjekte 9.3 Kollektive Schaffung informationeller Mehrwerte 9.4 Explikation von Wissensträgern 10 Konzeption des soziotechnischen Informationssystems 10.1 Aufbau und Grundlegung des Systems 10.2 Grammatisches Metamodell – das Template der Templates 10.3 Templates der Nutzerkontexte und Gültigkeiten 10.4 Konzeptuelle Templates des Rechercheprozesses 10.5 Konzeptuelle Templates der kollektiven Informationsarbeit 10.6 Das Object Repository und die realen Objekte 10.7 Anwendung, Vernetzung & Synopsis des Systems 11 Wissensrepräsentation und kontextbasierte Wiederverwendung 11.1 Grundlagen der Wissensrepräsentation 11.2 Barrieren der Kognition expliziter Wissensrepräsentationen 11.3 Repräsentationsformen bei kollektiver Informationsarbeit 11.4 Dissemination & Zusammenarbeit: Nutzung der Rechercheumgebung 11.5 Six Pieces kollektiver Informationsarbeit für die lernende Organisation SYNOPSIS 12 Erkenntnisse der Arbeit und kritische Würdigung 12.1 Grundlegende Erkenntnisse der theoriegeleiteten Exploration 12.2 Hypothesenbildung: Synopsis der Erfahrungen 12.3 Hypothesenprüfung: Machbarkeit des Systems