The Data Web has undergone a tremendous growth period. It currently consists of more then 3300 publicly available knowledge bases describing millions of resources from various domains, such as life sciences, government or geography, with over 89 billion facts. In the same way, the Document Web grew to the state where approximately 4.55 billion websites exist, 300 million photos are uploaded on Facebook as well as 3.5 billion Google searches are performed on average every day. However, there is a gap between the Document Web and the Data Web, since for example knowledge bases available on the Data Web are most commonly extracted from structured or semi-structured sources, but the majority of information available on the Web is contained in unstructured sources such as news articles, blog post, photos, forum discussions, etc. As a result, data on the Data Web not only misses a significant fragment of information but also suffers from a lack of actuality since typical extraction methods are time-consuming and can only be carried out periodically. Furthermore, provenance information is rarely taken into consideration and therefore gets lost in the transformation process. In addition, users are accustomed to entering keyword queries to satisfy their information needs. With the availability of machine-readable knowledge bases, lay users could be empowered to issue more specific questions and get more precise answers. In this thesis, we address the problem of Relation Extraction, one of the key challenges pertaining to closing the gap between the Document Web and the Data Web by four means. First, we present a distant supervision approach that allows finding multilingual natural language representations of formal relations already contained in the Data Web. We use these natural language representations to find sentences on the Document Web that contain unseen instances of this relation between two entities. Second, we address the problem of data actuality by presenting a real-time data stream RDF extraction framework and utilize this framework to extract RDF from RSS news feeds. Third, we present a novel fact validation algorithm, based on natural language representations, able to not only verify or falsify a given triple, but also to find trustworthy sources for it on the Web and estimating a time scope in which the triple holds true. The features used by this algorithm to determine if a website is indeed trustworthy are used as provenance information and therewith help to create metadata for facts in the Data Web. Finally, we present a question answering system that uses the natural language representations to map natural language question to formal SPARQL queries, allowing lay users to make use of the large amounts of data available on the Data Web to satisfy their information need.

In Web service composition, service discovery and combining suitable services through determination of interoperability among different services are important operations. Utilizing semantics improves the quality and facilitates automation of these operations. There are several previous approaches for semantic service discovery and service matching. In this work, we exploit and extend these semantic approaches in order to make Web service composition process more facilitated, less error prone and more automated. This work includes a service discovery and service interoperability checking technique which extends the previous semantic matching approaches. In addition to this, as a guidance system for the user, a new semantic domain model is proposed that captures semantic relations between concepts in various ontologies.

Schlagwörter wie „information overload“, „digital natives“ oder „digital immigrants“ prägen die heutige Informations- und Wissensgesellschaft. Zahlreiche wissenschaftliche Untersuchungen belegen zudem nachdrücklich, dass die technische Entwicklung in den nächsten Jahren noch rasanter fortschreitet als man es jemals vermuten durfte. Internet- Kommunikationsangeboten kommt bereits jetzt eine außergewöhnliche Bedeutung zu - mit steigender Tendenz. Außerdem werden Kommunikationsservices wie Web 2.0-Anwendungen als ein zunehmend wichtiger Faktor von Internetnutzung unterstrichen und der aktuelle Trend zur persönlichen Vernetzung über das Internet stets hervorgehoben. Die Bedeutung der Kernnutzungen des Internets als Inhaltsquelle und Kommunikationsform wird demnach auch weiterhin zunehmen. Diesem Trend müssen sich auch Klassifikationssysteme stellen. Die RVK hat mit dem im Oktober 2009 lancierten Web-Portal einen ersten Schritt in Richtung Vernetzung getan. Die bisher auf verschiedenen Internetseiten disparat untergebrachten Informationen zur RVK sowie die Datenbanken zur RVK sind nunmehr unter einer Oberfläche vereint, miteinander verknüpft und mit Elementen sozialer Software (RVK-Wiki zur größeren Transparenz bei Abstimmungsvorgängen) angereichert. Im Kontext des derzeit ebenfalls als beliebtes Schlagwort thematisierten Semantic Web ist das Portal der RVK ein Paradigmenwechsel in der langen Geschichte der RVK: Das gesamte Wissen zur RVK wird entsprechend seiner Bedeutung konzeptionell verbunden und bereits weitgehend maschinenlesbar (beispielsweise bezogen auf die Suchfunktion in der Datenbank RVK-Online) offeriert. Wissensmanagement sowie die Verbesserung der Qualität der umfangreichen Informationen zur RVK auf semantischer Ebene sind sehr verbessert worden, verbunden mit dem RVK-Wiki könnte man gar von einem ersten Impuls in Richtung Web 3.0 für die RVK sprechen. Auch die hierarchische Struktur der RVK trägt wesentlich zum Semantic Web bei, da in einer Klassifikation gerade hierarchische Strukturen zur „Ordnung“ des im Überfluss vorhandenen implizierten Wissens beitragen. Wesentlich ist demnach die Definition der Relationen im Web (und somit der entsprechenden Ontologien und Entitäten), um der Quantität der Angebote im World Wide Web auch entsprechend qualitativ hochwertige Services mit bibliothekarischem Mehrwert entgegenzusetzen. Für das Datenmodell des Semantic Web ist somit die Bereitstellung von nachhaltigen Normdaten wie es für die RVK ja angedacht - respektive fast umgesetzt ist – notwendig.

Semantic Web und Linked Data sind in aller Munde. Nach fast einem Jahrzehnt der Entwicklung der Technologien und Erforschung der Möglichkeiten des Semantic Webs rücken nun die Daten in den Mittelpunk, denn ohne diese wäre das Semantic Web nicht mehr als ein theoretisches Konstrukt. Fast wie das World Wide Web ohne Websites. Bibliotheken besitzen mit Normdaten (PND, SWD) und Titelaufnahmen eine Fülle Daten, die sich zur Befüllung des Semantic Web eignen und teilweise bereits für das Semantic Web aufbereitet und zur Nutzung freigegeben wurden. Die Universitätsbibliothek Mannheim hat sich in zwei verschiedenen Projekten mit der Nutzung solcher Daten befasst – allerdings standen diese zu diesem Zeitpunkt noch nicht als Linked Data zur Verfügung. In einem Projekt ging es um die automatische Erschließung von Publikationen auf der Basis von Abstracts, im anderen Projekt um die automatische Klassifikation von Publikationen auf der Basis von Titeldaten. Im Rahmen dieses Beitrags stellen wir die Ergebnisse der Projekte kurz vor, möchten aber im Schwerpunkt auf einen Nebenaspekt eingehen, der sich erst im Laufe dieser Projekte herauskristallisiert hat: Wie kann man die gewonnenen Ergebnisse dauerhaft und sinnvoll zur Nachnutzung durch Dritte präsentieren? Soviel vorweg: Beide Verfahren können und wollen einen Bibliothekar nicht ersetzen. Die Einsatzmöglichkeiten der generierten Daten sind vielfältig. Konkrete Einsätze, zum Beispiel das Einspielen in einen Verbundkatalog, sind aber aufgrund der Qualität und mangelnden Kontrolle der Daten umstritten. Die Bereitstellung dieser Daten als Linked Data im Semantic Web ist da eine naheliegende Lösung – jeder, der die Ergebnisse nachnutzen möchte, kann das tun, ohne dass ein bestehender Datenbestand damit kompromittiert werden könnte. Diese Herangehensweise wirft aber neue Fragen auf, nicht zuletzt auch nach der Identifizierbarkeit der Ursprungsdaten über URIs, wenn diese (noch) nicht als Linked Data zur Verfügung stehen. Daneben erfordert die Bereitstellung von Ergebnisdaten aber auch weitere Maßnahmen, die über die gängige Praxis von Linked Data hinaus gehen: Die Bereitstellung von Zusatzinformationen, die die Quelle und das Zustandekommen dieser Daten näher beschreiben (Provenienzinformationen), aber auch weitere Informationen, die über das zugrunde liegende Metadatenschema meist hinausgehen, wie Konfidenzwerte im Falle eines automatischen Verfahrens der Datenerzeugung. Dazu präsentieren wir Ansätze auf Basis von RDF Reification und Named Graphs und schildern die aktuellen Entwicklungen auf diesem Gebiet, wie sie zum Beispiel in der Provenance Incubator Group des W3C und in Arbeitsgruppen der Dublin Core Metadaten-Initiative diskutiert werden.

In this thesis, we present a hybrid semantic web service discovery framework that exploits both the signatures and specifications of a web service, whilst adopting logical and non-logical service matching methods. For signature level service matching, we have developed two techniques: (i) logical similarity measures applied to the services’ input/output concepts; and (b) non-logical matching based on a Structure Preserving Semantic Matching algorithm. For specification level service matching, we have applied a unique short sentence matching approach on the textual-description of a web service. We evaluated the performance of our S5 Web Service Matchmaker using the OWLS-TC dataset, and furthermore compared its performance with the OWLS-MX discovery model. Our results indicate that S5 Web Service Matchmaker offers an improved web service matching performance with a significant increase in recall and subtle improvements in precision. / Web services are independent software systems designed to offer machine-to-machine interactions over the WWW to achieve well-described operations. The description of a web service entails (a) a syntactic component detailing the service’s operations and data structures in terms of the Web Services Description Language (WSDL), and (b) a semantic component that offers a semantic description, in terms of an ontology, of the services’ data and operations. Typically, service providers expose their services to the public by providing brief descriptions of the service’s operations; the challenge is to discover the right service based on rather sparse service descriptions in response to a specific service request. In this thesis, we present a hybrid semantic web service discovery framework that offer semantic web service discovery at both the signature and specification levels of a web service, whilst exploiting logical and non-logical service matching methods. For signature level service matching, we have developed two techniques: (i) logical similarity measures applied to the services’ input/output concepts; and (b) non-logical matching based on a Structure Preserving Semantic Matching algorithm. For specification level service matching, we have applied a unique short sentence matching approach on the textual-description of a web service. The cumulative similarly measures determine the overall similarity of a services’ description with the service request. We evaluated the performance of our S5 Web Service Matchmaker using the OWLS-TC dataset, and furthermore compared its performance with the OWLS-MX discovery model. Our results indicate that S5 Web Service Matchmaker offers an improved web service matching performance with a significant increase in recall and subtle improvements in precision.

The Semantic Web is an extension of the current Web, where the availability of information is expected to enable the cooperation between man and, above all, machines. The creation of standards which express shared meaning enable the construction of applications to solve integration, collaboration and automation problems which were already been identified by scientific community and technology consumers. The use of Web Services has brought several advances in this sense, and their annotation in semantic terms, transforming them into Semantic Web Services, enables the Semantic Web intent. Several technologies also enable the creation of such elements and their inherent use as basic blocks of application development whose scope is embedded on Web. This way, due to the fast growing of the number of services, some approaches to effectively solve the problem of services integration and use become necessary. This work proposes a modeling of a software solution to the discovery and composition of Semantic Web Services problem through the use of a genetic algorithm based on abstract data types. It is also proposed a tool implementation using OWL, OWL-S and OWL-S API languages and frameworks as well as the formal problem definition along with the scientific community expectations to the given solution. / AWeb Semântica é uma ampliação da web atual onde a disposição da informação viabiliza a cooperação entre homens e, sobretudo, entre máquinas. O surgimento de padrões web que expressam significado compartilhado possibilitam a construção de aplicações que resolvem problemas de integração, colaboração e automação já identificados pela comunidade científica e mercado consumidor de tecnologias. A utilização de Serviços Web trouxe grandes ganhos neste sentido, e sua anotação em termos semânticos, tornando-os Serviços Web Semânticos, viabiliza a proposta da Web Semântica. Diversas tecnologias viabilizam a construção de tais elementos e sua conseqüente utilização como blocos básicos do desenvolvimento de aplicações cujo escopo é embarcado na web. Assim, dado o rápido crescimento da quantidade de serviços, tornam-se necessárias abordagens que resolvam de forma efetiva, com garantias de qualidade e tempo de resposta aceitável, a integração e posterior utilização destes. Este trabalho propõe a modelagem de uma solução de software para o problema da Descoberta e Composição de Serviços Web Semânticos através do uso do Algoritmo Genético Baseado em Tipos Abstratos de Dados. Também é proposta uma implementação utilizando OWL, OWL-S e a OWL-S API. São apresentadas a definição formal do problema, as expectativas da comunidade científica quanto às soluções elaboradas e os resultados obtidos com respeito à viabilidade da proposta.

As Semantic Web technology evolves many open areas emerge, which attract more research focus. In addition to quickly expanding Linked Open Data (LOD) cloud, various embeddable metadata formats (e.g. RDFa, microdata) are becoming more common. Corporations are already using existing Web of Data to create new technologies that were not possible before. Watson by IBM an artificial intelligence computer system capable of answering questions posed in natural language can be a great example. On the other hand, ubiquitous devices that have a large number of sensors and integrated devices are becoming increasingly powerful and fully featured computing platforms in our pockets and homes. For many people smartphones and tablet computers have already replaced traditional computers as their window to the Internet and to the Web. Hence, the management and presentation of information that is useful to a user is a main requirement for today’s smartphones. And it is becoming extremely important to provide access to the emerging Web of Data from the ubiquitous devices. In this thesis we investigate how ubiquitous devices can interact with the Semantic Web. We discovered that there are five different approaches for bringing the Semantic Web to ubiquitous devices. We have outlined and discussed in detail existing challenges in implementing this approaches in section 1.2. We have described a conceptual framework for ubiquitous semantic applications in chapter 4. We distinguish three client approaches for accessing semantic data using ubiquitous devices depending on how much of the semantic data processing is performed on the device itself (thin, hybrid and fat clients). These are discussed in chapter 5 along with the solution to every related challenge. Two provider approaches (fat and hybrid) can be distinguished for exposing data from ubiquitous devices on the Semantic Web. These are discussed in chapter 6 along with the solution to every related challenge. We conclude our work with a discussion on each of the contributions of the thesis and propose future work for each of the discussed approach in chapter 7.

In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of datasets published on the Web and make this quality information explicit. Assessing data quality is particularly a challenge in LD as the underlying data stems from a set of multiple, autonomous and evolving data sources. Moreover, the dynamic nature of LD makes assessing the quality crucial to measure the accuracy of representing the real-world data. On the document Web, data quality can only be indirectly or vaguely defined, but there is a requirement for more concrete and measurable data quality metrics for LD. Such data quality metrics include correctness of facts wrt. the real-world, adequacy of semantic representation, quality of interlinks, interoperability, timeliness or consistency with regard to implicit information. Even though data quality is an important concept in LD, there are few methodologies proposed to assess the quality of these datasets. Thus, in this thesis, we first unify 18 data quality dimensions and provide a total of 69 metrics for assessment of LD. The first methodology includes the employment of LD experts for the assessment. This assessment is performed with the help of the TripleCheckMate tool, which was developed specifically to assist LD experts for assessing the quality of a dataset, in this case DBpedia. The second methodology is a semi-automatic process, in which the first phase involves the detection of common quality problems by the automatic creation of an extended schema for DBpedia. The second phase involves the manual verification of the generated schema axioms. Thereafter, we employ the wisdom of the crowds i.e. workers for online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) to assess the quality of DBpedia. We then compare the two approaches (previous assessment by LD experts and assessment by MTurk workers in this study) in order to measure the feasibility of each type of the user-driven data quality assessment methodology. Additionally, we evaluate another semi-automated methodology for LD quality assessment, which also involves human judgement. In this semi-automated methodology, selected metrics are formally defined and implemented as part of a tool, namely R2RLint. The user is not only provided the results of the assessment but also specific entities that cause the errors, which help users understand the quality issues and thus can fix them. Finally, we take into account a domain-specific use case that consumes LD and leverages on data quality. In particular, we identify four LD sources, assess their quality using the R2RLint tool and then utilize them in building the Health Economic Research (HER) Observatory. We show the advantages of this semi-automated assessment over the other types of quality assessment methodologies discussed earlier. The Observatory aims at evaluating the impact of research development on the economic and healthcare performance of each country per year. We illustrate the usefulness of LD in this use case and the importance of quality assessment for any data analysis.

Enterprise crowdsourcing transforms the way in which traditional business tasks can be processed by harnessing the collective intelligence and workforce of a large and often diver-sified group of people. At the present time, data and information residing within enterprise crowdsourcing systems and other business applications are insufficiently interlinked and are rarely made publicly available in an open and semantically structured manner – neither to the corporate intranet nor to the World Wide Web (WWW). However, the semantic annotation of enterprise crowdsourcing activities is a promising research and application domain. The Semantic Web and its related technologies, methods and principles for publishing structured data offer an extension of the traditional layout-oriented Web to provide more intelligent and complex services. This technical report describes the efforts toward a universal and lightweight yet powerful Semantic Web vocabulary for the domain of enterprise crowdsourcing. As a methodology for developing the vocabulary, the approach of ontology engineering is applied. To illustrate the purpose and to limit the scope of the ontology, several informal competency questions as well as functional and non-functional requirements are presented. The subsequent con-ceptualization of the ontology applies different sources of knowledge and considers various perspectives. A set of semantic entities is derived from a review of existing crowdsourcing applications and a review of recent crowdsourcing literature. During the domain capture, all partial results of the review are integrated into a consistent data dictionary and structured as a UML data schema. The designed ontology includes 24 classes, 22 object properties and 30 datatype properties to describe the key aspects of a crowdsourcing model (CSM). To demonstrate the technical feasibility, the ontology is implemented using the Web Ontology Language (OWL). Finally, the ontology is evaluated by means of transforming informal to formal competency questions, comparing it to existing semantic vocabularies, and calculat-ing ontology metrics. Evidence is shown that the CSM ontology covers the key representa-tional needs of the enterprise crowdsourcing domain. At the end of the technical report, cur-rent limitations are illustrated and directions for future research are proposed.:Table of Contents I List of Figures III List of Tables IV List of Code Listings V List of Abbreviations VI Abstract VIII 1 Introduction 1 2 Research Objective 4 3 Ontology Engineering 6 4 Purpose and Scope 9 4.1 Informal Competency Questions 10 4.2 Requirements 11 4.2.1 Functional Requirements 12 4.2.2 Non-Functional Requirements 15 5 Ontology Development 18 5.1 Conceptualization 18 5.1.1 System Review 18 5.1.2 Literature Review 21 5.2 Domain Capture 26 5.3 Integration 28 5.3.1 Semantic Vocabularies and Standards 28 5.3.2 Implications for the Design 33 5.4 Implementation 33 6 Evaluation 35 6.1 Transforming Informal to Formal Competency Questions 36 6.2 Comparing the Ontology to other Semantic Vocabularies 42 6.3 Calculating Ontology Metrics 44 7 Conclusion 46 8 References 48 Appendix A (System Review) i Appendix B (Crowdsourcing Taxonomies) v Appendix C (Data Dictionary) ix Appendix D (Semantic Vocabularies) xi Appendix E (CSM Ontology Source Code) xv Appendix F (Sample Data Instance 1) xxxi Appendix G (Sample Data Instance 2) xxxiv

The last couple of years have seen a fascinating evolution. While the early Web predominantly focused on human consumption of Web content, the widespread dissemination of social software and Web 2.0 technologies enabled new forms of collaborative content creation and problem solving. These new forms often utilize the principles of collective intelligence, a phenomenon that emerges from a group of people who either cooperate or compete with each other to create a result that is better or more intelligent than any individual result (Leimeister, 2010; Malone, Laubacher, & Dellarocas, 2010). Crowdsourcing has recently gained attention as one of the mechanisms that taps into the power of web-enabled collective intelligence (Howe, 2008). Brabham (2013) defines it as “an online, distributed problem-solving and production model that leverages the collective intelligence of online communities to serve specific organizational goals” (p. xix). Well-known examples of crowdsourcing platforms are Wikipedia, Amazon Mechanical Turk, or InnoCentive. Since the emergence of the term crowdsourcing in 2006, one popular misconception is that crowdsourcing relies largely on an amateur crowd rather than a pool of professional skilled workers (Brabham, 2013). As this might be true for low cognitive tasks, such as tagging a picture or rating a product, it is often not true for complex problem-solving and creative tasks, such as developing a new computer algorithm or creating an impressive product design. This raises the question of how to efficiently allocate an enterprise crowdsourcing task to appropriate members of the crowd. The sheer number of crowdsourcing tasks available at crowdsourcing intermediaries makes it especially challenging for workers to identify a task that matches their skills, experiences, and knowledge (Schall, 2012, p. 2). An explanation why the identification of appropriate expert knowledge plays a major role in crowdsourcing is partly given in Condorcet’s jury theorem (Sunstein, 2008, p. 25). The theorem states that if the average participant in a binary decision process is more likely to be correct than incorrect, then as the number of participants increases, the higher the probability is that the aggregate arrives at the right answer. When assuming that a suitable participant for a task is more likely to give a correct answer or solution than an improper one, efficient task recommendation becomes crucial to improve the aggregated results in crowdsourcing processes. Although some assumptions of the theorem, such as independent votes, binary decisions, and homogenous groups, are often unrealistic in practice, it illustrates the importance of an optimized task allocation and group formation that consider the task requirements and workers’ characteristics. Ontologies are widely applied to support semantic search and recommendation mechanisms (Middleton, De Roure, & Shadbolt, 2009). However, little research has investigated the potentials and the design of an ontology for the domain of enterprise crowdsourcing. The author of this thesis argues in favor of enhancing the automation and interoperability of an enterprise crowdsourcing environment with the introduction of a semantic vocabulary in form of an expressive but easy-to-use ontology. The deployment of a semantic vocabulary for enterprise crowdsourcing is likely to provide several technical and economic benefits for an enterprise. These benefits were the main drivers in efforts made during the research project of this thesis: 1. Task allocation: With the utilization of the semantics, requesters are able to form smaller task-specific crowds that perform tasks at lower costs and in less time than larger crowds. A standardized and controlled vocabulary allows requesters to communicate specific details about a crowdsourcing activity within a web page along with other existing displayed information. This has advantages for both contributors and requesters. On the one hand, contributors can easily and precisely search for tasks that correspond to their interests, experiences, skills, knowledge, and availability. On the other hand, crowdsourcing systems and intermediaries can proactively recommend crowdsourcing tasks to potential contributors (e.g., based on their social network profiles). 2. Quality control: Capturing and storing crowdsourcing data increases the overall transparency of the entire crowdsourcing activity and thus allows for a more sophisticated quality control. Requesters are able to check the consistency and receive appropriate support to verify and validate crowdsourcing data according to defined data types and value ranges. Before involving potential workers in a crowdsourcing task, requesters can also judge their trustworthiness based on previous accomplished tasks and hence improve the recruitment process. 3. Task definition: A standardized set of semantic entities supports the configuration of a crowdsourcing task. Requesters can evaluate historical crowdsourcing data to get suggestions for equal or similar crowdsourcing tasks, for example, which incentive or evaluation mechanism to use. They may also decrease their time to configure a crowdsourcing task by reusing well-established task specifications of a particular type. 4. Data integration and exchange: Applying a semantic vocabulary as a standard format for describing enterprise crowdsourcing activities allows not only crowdsourcing systems inside but also crowdsourcing intermediaries outside the company to extract crowdsourcing data from other business applications, such as project management, enterprise resource planning, or social software, and use it for further processing without retyping and copying the data. Additionally, enterprise or web search engines may exploit the structured data and provide enhanced search, browsing, and navigation capabilities, for example, clustering similar crowdsourcing tasks according to the required qualifications or the offered incentives.:Summary: Hetmank, L. (2014). Enhancing Automation and Interoperability in Enterprise Crowdsourcing Environments (Summary). Article 1: Hetmank, L. (2013). Components and Functions of Crowdsourcing Systems – A Systematic Literature Review. In 11th International Conference on Wirtschaftsinformatik (WI). Leipzig. Article 2: Hetmank, L. (2014). A Synopsis of Enterprise Crowdsourcing Literature. In 22nd European Conference on Information Systems (ECIS). Tel Aviv. Article 3: Hetmank, L. (2013). Towards a Semantic Standard for Enterprise Crowdsourcing – A Scenario-based Evaluation of a Conceptual Prototype. In 21st European Conference on Information Systems (ECIS). Utrecht. Article 4: Hetmank, L. (2014). Developing an Ontology for Enterprise Crowdsourcing. In Multikonferenz Wirtschaftsinformatik (MKWI). Paderborn. Article 5: Hetmank, L. (2014). An Ontology for Enhancing Automation and Interoperability in Enterprise Crowdsourcing Environments (Technical Report). Retrieved from http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-155187.