Bøger af Philipp Cimiano

Knowledge Management and Knowledge Engineering is a fascinating ?eld of re- 1 search these days. In the beginning of EKAW, the modeling and acquisition of knowledge was the privilege of - or rather a burden for - a few knowledge engineers familiar with knowledge engineering paradigms and knowledge rep- sentationformalisms.While the aimhasalwaysbeentomodelknowledgedecl- atively and allow for reusability, the knowledge models produced in these early days were typically used in single and very speci?c applications and rarely - changed. Moreover, these models were typically rather complex, and they could be understood only by a few expert knowledge engineers. This situation has changed radically in the last few years as clearly indicated by the following trends: - The creation of (even formal) knowledge is now becoming more and more collaborative. Collaborative ontology engineering tools and social software platforms show the potential to leverage the wisdom of the crowds (or at least of "the many") to lead to broader consensus and thus produce shared models which qualify better for reuse. - A trend can also be observed towards developing and publishing small but 2 3 4 high-impactvocabularies(e.g., FOAF, DublinCore, GoodRelations)rather than complex and large knowledge models.

For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies

This book constitutes the thoroughly refereed post-proceedings of the satellite events of the10th International Conference on the Semantic Web, ESWC 2013, held in Montpellier, France, in May 2013. The volume contains 44 papers describing the posters and demonstrations, 10 best workshop papers selected from various submissions and four papers of the AI Mashup Challenge. The papers cover various aspects on the Semantic Web.

Ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Suitable for research scientists and others, this book discusses ontologies for the semantic web, knowledge management, information retrieval, text clustering and classification, as well as natural language processing.