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Abstract

An ontology is a knowledge representation structure made up of concepts and their interrelations. It represents shared understanding delineated by some domain. The building of an ontology can be addressed from the perspective of natural language processing. This thesis discusses the validity and theoretical background of knowledge acquisition from natural language. It also presents the theoretical and experimental framework for NLP-driven ontology building and evaluation tasks.

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Content

Inhalt: Boolean retrieval - The term vocabulary & postings lists - Dictionaries and tolerant retrieval - Index construction - Index compression - Scoring, term weighting & the vector space model - Computing scores in a complete search system - Evaluation in information retrieval - Relevance feedback & query expansion - XML retrieval - Probabilistic information retrieval - Language models for information retrieval - Text classification & Naive Bayes - Vector space classification - Support vector machines & machine learning on documents - Flat clustering - Hierarchical clustering - Matrix decompositions & latent semantic indexing - Web search basics - Web crawling and indexes - Link analysis Vgl. die digitale Fassung unter: http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf.