Refine
Year of publication
Document Type
- Doctoral Thesis (434)
- Part of Periodical (136)
- Book (45)
- Article (43)
- Report (20)
- Preprint (16)
- Conference Proceeding (10)
- Master's Thesis (10)
- Other (8)
- Bachelor Thesis (7)
Language
- German (447)
- English (277)
- Multiple languages (5)
- Spanish (3)
- French (1)
Has Fulltext
- yes (733)
Keywords
- Lehrerbildung (28)
- Deutschland (22)
- Österreich (18)
- Universität Passau (13)
- Kultursemiotik (12)
- Maßtheorie (12)
- Mediensemiotik (12)
- Graphenzeichnen (9)
- Computersicherheit (8)
- Marketing (7)
Institute
- Philosophische Fakultät (205)
- Fakultät für Informatik und Mathematik (108)
- Mitarbeiter Lehrstuhl/Einrichtung der Fakultät für Informatik und Mathematik (70)
- Wirtschaftswissenschaftliche Fakultät (63)
- Mitarbeiter Lehrstuhl/Einrichtung der Wirtschaftswissenschaftlichen Fakultät (40)
- Juristische Fakultät (38)
- Zentrum für Lehrerbildung und Fachdidaktik (30)
- Universitätsbibliothek (28)
- Philosophische Fakultät / Pädagogik (23)
- Sonstiger Autor der Wirtschaftswissenschaftlichen Fakultät (11)
Multinationale Unternehmen (MNUs) sind durch ihre grenzüberschreitende Tätigkeit in besonderem Maße mit nicht immer kompatiblen Forderungen und Erwartungen unterschiedlichen institutionell-kulturellen Ursprungs konfrontiert. Dies gilt insbesondere auch für ihr internationales Corporate Social Responsibility (CSR)-Management. Die vorliegende Dissertation geht daher der Frage nach, wie MNUs in ihrem grenzüberschreitenden CSR-Management mit eben dieser institutionellen Komplexität umgehen – d.h. wie sie es entlang von Identitäten, Praktiken und Strukturen organisieren und damit ihre Antworten auf bestimmte institutionelle Forderungen und Erwartungen gestalten? Die hierfür durchgeführte, vergleichende Mehrfallstudie kommt diesbezüglich zu drei zentralen Ergebnissen: (1) Zum einen variiert der Umgang mit institutioneller Komplexität derzeit entlang unterschiedlicher innerhalb der MNUs gleichzeitig realisierter Typen grenzüberschreitenden CSR-Managements. (2) Zum anderen fungiert die CSR-Identität der MNUs als ein diese Typen organisierender institutioneller Filter. (3) Da der institutionelle Bezugspunkt der CSR-Identitäten allerdings zunehmend ein transnationaler ist, führt dieser Mechanismus auch zu einer sukzessiven, praktisch-strukturellen Vereinheitlichung grenzüberschreitenden CSR-Managements; unterschiedliche Erwartungen beispielsweise nationalen Ursprungs treten damit für die untersuchten MNUs schrittweise in den Hintergrund. Damit stellt diese Arbeit nicht nur die Homogenitätsannahme bisheriger CSR-Internationalisierungsstudien infrage. Ihr Einblick in die selektive Bewältigung institutioneller Komplexität verweist auch auf die Schlüsselrolle von CSR-Identitäten sowie auf die wachsende Bedeutung transnationaler Institutionen. Damit trägt vorliegende Dissertation maßgeblich sowohl zur internationalen als auch zur institutionellen CSR-Forschung bei.
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms.
DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings
(2016)
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. In this work, we propose DoSeR (Disambiguation of Semantic Resources), a (named) entity disambiguation framework that is knowledge-base-agnostic in terms of RDF (e.g. DBpedia) and entity-annotated document knowledge bases (e.g. Wikipedia). Initially, our framework automatically generates semantic entity embeddings given one or multiple knowledge bases. In the following, DoSeR accepts documents with a given set of surface forms as input and collectively links them to an entity in a knowledge base with a graph-based approach. We evaluate DoSeR on seven different data sets against publicly available, state-of-the-art (named) entity disambiguation frameworks. Our approach outperforms the state-of-the-art approaches that make use of RDF knowledge bases and/or entity-annotated document knowledge bases by up to 10% F1 measure.
Entity Linking is the task of mapping terms in arbitrary documents to entities in a knowledge base by identifying the correct semantic meaning. It is applied in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question and Answering. Most existing Entity Linking systems were optimized for specific domains (e.g., general domain, biomedical domain), knowledge base types (e.g., DBpedia, Wikipedia), or document structures (e.g., tables) and types (e.g., news articles, tweets). This led to very specialized systems that lack robustness and are only applicable for very specific tasks. In this regard, this work focuses on the research and development of a robust Entity Linking system in terms of domains, knowledge base types, and document structures and types.
To create a robust Entity Linking system, we first analyze the following three crucial components of an Entity Linking algorithm in terms of robustness criteria: (i) the underlying knowledge base, (ii) the entity relatedness measure, and (iii) the textual context matching technique. Based on the analyzed components, our scientific contributions are three-fold. First, we show that a federated approach leveraging knowledge from various knowledge base types can significantly improve robustness in Entity Linking systems. Second, we propose a new state-of-the-art, robust entity relatedness measure for topical coherence computation based on semantic entity embeddings. Third, we present the neural-network-based approach Doc2Vec as a textual context matching technique for robust Entity Linking.
Based on our previous findings and outcomes, our main contribution in this work is DoSeR (Disambiguation of Semantic Resources). DoSeR is a robust, knowledge-base-agnostic Entity Linking framework that extracts relevant entity information from multiple knowledge bases in a fully automatic way. The integrated algorithm represents a collective, graph-based approach that utilizes semantic entity and document embeddings for entity relatedness and textual context matching computation. Our evaluation shows, that DoSeR achieves state-of-the-art results over a wide range of different document structures (e.g., tables), document types (e.g., news documents) and domains (e.g., general domain, biomedical domain). In this context, DoSeR outperforms all other (publicly available) Entity Linking algorithms on most data sets.
The well-founded semantics has been accepted as the most relevant semantics for logic-based information systems. In this dissertation a framework based on a set of program transformations is presented that generalizes all major computation approaches for the well-founded semantics using a common data structure and provides a common language to describe their evaluation strategy. This rewriting system gives the formal background to analyze and combine different evaluation strategies in a common framework, or to design new algorithms and prove the correctness of its implementations at a high level just by changing the order of program transformations.
Der Wald des Mittelalters
(2019)
‚Der Wald des Mittelalters‘ soll im Folgenden nicht allein als historisches Dokument beschäftigen – als kultureller Text im 11. bis 13. Jahrhundert, dessen Bedeutung von einem System symbolischer Formen (Ernst Cassirer) oder den Organisationsniveaus einer Semiosphäre (Jurij M. Lotman) bestimmt wird. Ergänzt um den Faktor des historischen Archivs steht darüber hinaus die Form seiner Erinnerung zur Diskussion, wodurch er vielmehr als historisches Monument erscheint (Michel Foucault), als Palimpsest, dessen verblasste Inschriften Zeugnis vom Wechsel historischer Vorschriften abgeben.
Die nachhaltige Transformation der Energiesysteme wird in politischen wie gesellschaftlichen Diskussionen häufig auf die Parole „weg von fossil-atomaren hin zu erneuerbaren Energieformen“ reduziert. Dies allein wird dem Leitbild allerdings nicht zwingend gerecht. Zum einen erfüllen auch erneuerbare Energien nicht in jeder Region die Kriterien der Nachhaltigkeit und zum anderen gibt die bloße Forderung nach mehr erneuerbaren Energien noch keine Orientierung bezüglich der konkreten Umsetzung. Wo die neuen Energieformen gebaut werden sollen und wie die Technologien Bioenergie, Geothermie, Photovoltaik, Wasserkraft oder Windenergie in bestehende Versorgungsstrukturen eingebunden werden können, bleibt folglich unbeantwortet. Gleichzeitig führt die einseitige Beachtung von ökonomischer Rentabilität bzw. staatlicher Subventionierung häufig zu räumlichen Fehlallokationen und gesellschaftlichen Friktionen. Die Arbeit will deshalb einen wissenschaftlich fundierten und zugleich praktikablen Weg zur Gestaltung einer regionalen nachhaltigen Energieversorgung aufzeigen. Nachhaltigkeit ist demzufolge bereits in die konzeptionelle Entwicklung der Energiestrategie zu integrieren und anschließend für die praktische Anwendung zu operationalisieren. Aufbauend auf der Suche nach nachhaltigen Strukturen in der Energieversorgung wird eine neue Zieldefinition von nachhaltiger Energieversorgung formuliert, um anschließend das Konzept doppelter Nachhaltigkeit zu entwickeln. Vorgabe der Zieldefinition ist es, die Bereiche der Technologiebewertung und der Potenzial- bzw. Standortsuche in einem räumlichen Kontext gemeinsam zu betrachten. Neben der Frage, welche Technologien für eine nachhaltige Versorgung in einer Region am besten geeignet sind, rückt das Konzept bei der Standortplanung die Suche nach ökonomisch rentablen, ökologisch verträglichen und sozial akzeptierten Orten in den Mittelpunkt. Damit wird eine Energiestrategie vorgestellt, die, orientiert an den Leitlinien einer nachhaltigen Entwicklung, einen Weg aufzeigt, wie kommunale und regionale Energieversorgungsstrukturen räumlich gestaltet und anhand von Nachhaltigkeitskriterien optimiert werden können. Angewendet wird das Konzept doppelter Nachhaltigkeit exemplarisch für den Regierungsbezirk Niederbayern.
Data has become a necessary resource for firm operations in the modern digital world, explaining their growing data gathering efforts. Due to this development, consumers are confronted with decisions to disclose personal data on a daily basis, and have become increasingly intentional about data sharing. While this reluctance to disclose personal data poses challenges for firms, at the same time, it also creates new opportunities for improving privacy-related interactions with customers. This dissertation advocates for a more holistic perspective on consumers’ privacy-related decision-making and introduces the consumer privacy journey consisting of three subsequent phases: pre data disclosure, data disclosure, post data disclosure. In three independent essays, I stress the importance of investigating data requests (i.e., the first step of this journey) as they represent a largely neglected, yet, potentially powerful means to influence consumers’ decision-making and decision-evaluation processes. Based on dual-processing models of decision-making, this dissertation focuses on both consumers’ cognitive and affective evaluations of privacy-related information: First, Essay 1 offers novel conceptualizations and operationalizations of consumers’ perceived behavioral control over personal data (i.e., cognitive processing) in the context of Artificial Intelligence (AI)-based data disclosure processes. Next, Essay 2 examines consumers’ cognitive and affective processing of a data request that entails relevance arguments as well as relevance-illustrating game elements. Finally, Essay 3 categorizes affective cues that trigger consumers’ affective processing of a data request and proposes that such cues need to fit with a specific data disclosure situation to foster long-term decision satisfaction. Collectively, my findings provide research and practice with new insights into consumers’ privacy perceptions and behaviors, which are particularly valuable in the context of complex, new (technology-enabled) data disclosure situations.
Die Schriften zur Kultur- und Mediensemiotik | Online sind ein Open Access Journal des Virtuellen Zentrums für kultursemiotische Forschung / Virtual Centre for Cultural Semiotics (www.kultursemiotik.com). Ausgabe 7 beleuchtet unterschiedliche Potenziale der Semiotik in der Lehrer*innenbildung, die aus theoretischer Perspektive aufgezeigt oder anhand von Konzepten aus der Praxis mit theoretischer/empirischer Fundierung untersucht werden. Beides findet aus fachlicher, überfachlicher, inter- oder transdisziplinärer Perspektive statt.
Governments around the world currently focus on shaping the digital economy. Particular attention is paid to Internet platforms, Internet infrastructure and data as essential components of the digital economy. The three studies in this thesis contribute to the understanding of the behavior of firms in each of these domains and derive insights for future regulations and business projects.
The first study deals with the ranking of content on Internet platforms and how it affects the incentives of content providers to invest in content quality. The focus of the study is on sponsored ranking and organic ranking, but the case that a vertically integrated content provider is favored by an Internet platform is also taken into account. Using a game theoretic model, it is shown that there is no ranking design that strictly leads to more investment compared to the other designs. It is also shown that the Internet platform usually chooses the type of ranking that, from the perspective of the Internet platform and consumers, yields the best expected overall content quality. The second study deals with the incentive of Internet service providers to throttle specific Internet content. The key finding is that Internet service providers use this instrument to utilize the capacity of their telecommunications network more efficiently. This leads not only to more benefits for Internet users, but also to a higher incentive to invest in network capacity due to better monetization. The third study examines the circumstances under which firms are willing to share data with other firms. By means of an economic laboratory experiment, it is shown that more data is shared if the firms have control over who exactly they share data with. Thus, for example, data pools that grant unrestricted data access to all participating firms can be expected to perform worse than data pools that give their participating firms control over with whom their uploaded data is shared. In addition, the third study finds that established relationships are characterized by more data sharing and less volatility in the amount of shared data than new relationships. The study concludes that data sharing projects should not be expected to work optimally right away.
In summary, the studies in this thesis identify a number of costs that may arise when digital firms' choice is restricted by regulation or design. The ability of Internet service providers to throttle certain content and the ability of Internet platforms to choose the ranking design are usually used in the best interests of consumers. Data sharing also works best when firms are free to decide who gets their data.