Local variations of place names are typically not present in official place name directories. Spatially-aware information retrieval tools or navigation systems rely on these gazetteers to resolve the place names into coordinates. Place names commonly appear in user-generated information on the Web, for example as tags used to describe the content of a picture. Local variations are expected to exist in the long tail of the frequency distribution of terms appearing in user-generated annotations. This thesis explores whether these annotations are a useful source to automatically acquire not officially recognised place names. It presents a methodology analysing point patterns to assess if a term refers to a place. These point patterns represent the spatial and temporal coverage of a term. A cluster analysis further separates point patterns capturing the extent of ambiguous place names. The resulting dictionary of place names can be an invaluable source for any application where users are expected to use place names in search queries.

Ontological modelling today is applied in many areas of science and technology, including the Semantic Web. The W3C standard OWL defines one of the most important ontology languages based on the semantics of description logics. An alternative is to use rule languages in knowledge modelling, as proposed in the W3C’s RIF standard. So far, it has often been unclear how to combine both technologies without sacrificing essential computational properties.This book explains this problem and presents new solutions that have recently been proposed. Extensive introductory chapters provide the necessary background for understanding the goals and challenges of this field, whereas advanced chapters discuss novel solutions in full detail. Enriched knowledge representation languages that are introduced include DL Rules, Horn description logics, and DL+safe Rules. In each of these cases, emphasis is put on finding a favourable trade-off between expressiveness and computational complexity. This naturally leads to the light-weight DL rule language ELP which illustrates that expressive ontological modelling and tractable inferencing can indeed go together. Comprehensive references for further reading are provided throughout the book.

".eine wunderbare Sammlung von Sichtweisen auf die Informatik, wobei durchgängig vermeintliche Vorurteile über die Informatik als Ausgangspunkt genommen werden, um einen informatikrelevanten Themenbereich an zu sprechen, zu kommentieren und ggf. auf wichtige Literatur hinzuweisen." Prof. Dr. Jörg Desel, Universität Eichstätt "ist die Informatik an allem schuld, wenn über Computer und Internet geklagt wird? Dass das nicht so ist, sondern auf viel komplexeren, auch historischen und menschlichen Gegebenheiten beruht, erklärt dieses Buch aus verschiedenen Perspektiven, nämlich aus der Sicht von unerfahrenen Nutzern und von Profis ebenso wie unter den Aspekten von Wirtschaft, Gesellschaft und Wissenschaft." Prof. Dr. Heidi Heilmann, Überlingen am Bodensee

The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult in the case of complex knowledge types, like processes. The analysis of different application domains uncovers that process knowledge is one of the most frequent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasingly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretations of process executions.