The exponential growth of digital information available in companies and on the Web creates the need for search tools that can respond to the most sophisticated information needs. Many user tasks would be simplified if Search Engines would support typed search, and return entities instead of just Web documents. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic.In the first part of the book, we propose a model for expert finding based on the well-consolidated vector space model for Information Retrieval and investigate its effectiveness.In the second part of the book, we investigate different methods based on Semantic Web and Natural Language Processing techniques for ranking entities of different types both in Wikipedia and, generally, on the Web.In the third part of this thesis, we study the problem of Entity Retrieval for news applications and the importance of the news trail history (i.e., past related articles) to determine the relevant entities in current articles.We also study opinion evolution about entities: We propose a method for automatically extracting the public opinion about political candidates from the blogosphere.

With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representa-tion and reasoning. However, recent progress in the field faces a lack of well-structured ontologies with large amounts of instance data due to the fact that engineering such ontologies requires a considerable investment of resources. Nowadays, knowledge bases often provide large volumes of data without so-phisticated schemata. Hence, methods for automated schema acquisition and maintenance are sought. Schema acquisition is closely related to solving typi-cal classification problems in machine learning, e.g. the detection of chemical compounds causing cancer. In this work, we investigate both, the underlying machine learning techniques and their application to knowledge acquisition in the Semantic Web.

Dies ist der Tagungsband der wissenschaftlichen Beiträge der „Geoinformatik 2009“, die vom 31. März bis zum 2. April 2009 unter dem Motto „Nutzen der Geoinformatik für Umwelt und Gesellschaft“ in Osnabrück stattfand. Die „Geoinformatik“ ist eine neu etablierte Tagungsreihe, die die wachsende Bedeutung der Geoinformatik in Gesellschaft und Wissenschaft aktiv aufgreift und dabei helfen will ihre Zukunft mitzugestalten. Die Tagung richtete sich an Vertreter aus Wissenschaft, Wirtschaft und Verwaltungen, d. h. an Entscheidungsträger aus allen Branchen, Anwender, Praktiker, Forscher, Firmenvertreter sowie Studierende. Die Tagung ging hervor aus den GI-Tagen (GI-days) der Universität Münster und den GI-Tagen Nord des GIN e.V. und stellte damit erstmalig Geoinformatikern in Deutschland, Österreich und der Schweiz eine überregionale Diskussionsplattform zur Verfügung. Begleitend fand während der Konferenz die Jahresversammlung der Gesellschaft für Geoinformatik (GfGi) statt.

Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about geographic occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. Here, questions that call for clarification are: How can geographic occurrences be formally modeled with respect to properties observed by sensors? How can the formal model be used to infer knowledge about geographic occurrences from sensor observations? This book delivers a formal model to answer these ontological questions. The model is used to infer information about blizzards from time series produced by a weather station.

The general objective of the thesis is to provide domain independent, and resource independent methods and tools for speeding up the ontology development process and is achieved by reusing and re-engineering as much as possible available non-ontological resources (NORs). To fulfil this overall goal, we have decomposed it into the following methodological and technological objectives: The definition of methodological aspects related to the reuse of non-ontolo-gical resources for building ontologies. The definition of methodological aspects related to the re-engineering of non-ontological resources for building ontologies. The creation of a library of patterns for re-engineering non-ontological resources into ontologies. The development of a software library, NOR2O, that implements the suggestions given by the re-engineering patterns.

Mathematics is becoming increasingly collaborative, but software does not sufficiently support that: Social Web applications do not currently make mathematical knowledge accessible to automated agents that have a deeper understanding of mathematical structures. Such agents exist but focus on individual research tasks, such as authoring, publishing, peer-review, or verification, instead of complex collaboration workflows. This work effectively enables their integration by bridging the document-oriented perspective of mathematical authoring and publishing, and the network perspective of threaded discussions and Web information retrieval. This is achieved by giving existing representations of mathematical and relevant related knowledge about applications, projects and people a common Semantic Web foundation. Service integration is addressed from the two perspectives of enriching published documents by embedding assistive services, and translating between different knowledge representations inside knowledge bases. A usability evaluation of a semantic wiki that coherently integrates knowledge production and consumption services points out the remaining challenges in making such heterogeneously integrated environments support realistic workflows. The results of this thesis will soon also enable collaborative acquisition of new mathematical knowledge, as well as the contribution of existing knowledge collections to the Web of Data.

Diese Arbeit beschäftigt sich mit der Frage, wie eine Infrastruktur für digitale Objektgedächtnisse aussehen muss. Ziel der Arbeit ist die Erstellung eines Architekturkonzepts für die Repräsentation und die Verwaltung von, sowie den Zugriff auf digitale Objektgedächtnisse inklusive abgestimmter Werkzeuge, die die Erstellung neuer und die Migration bestehender Systeme erleichtern.Aufgrund der heterogenen Datenlandschaft in einem „open-loop“-Szenario, werden an ein Objektgedächtnis besondere Anforderungen gestellt. Zum einen muss es flexibel genug ausgelegt sein, um mit den unterschiedlichsten Daten umgehen zu können. Zum anderen muss ein einfacher und strukturierter Zugriff auf die Daten ermöglicht werden, der je nach Anwendungsfall auch mit Konzepten des rechte- und rollenbasierten Zugriffs und der Versionsverwaltung ergänzt werden muss.Diese Arbeit zeigt daher zuerst ein Datenmodell zur Strukturierung von Objektgedächtnisdaten. Dieses erlaubt mit Hilfe von Metadaten auch in heterogenen Szenarien einen domänenübergreifenden Datenaustausch. Im Anschluss wird eine Softwarearchitektur vorgestellt, die eine Speicher- und Zugriffslösung für Objektgedächtnisse anbietet und die Gedächtnisse zusätzlich mit einer Aktivitätskomponente ausstattet. Abgerundet wird das Konzept mit einem Satz an Werkzeugen, die es Entwicklern erlauben, zum Beispiel bestehende Datenbestände zu migrieren, neue Strukturen auf Basis von semantischen Daten aufzubauen und diese mit anwendungsspezifischen Visualisierungen dem Benutzer wieder zugänglich zu machen.