Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Konzept für ein thesaurusbasiertes Information Retrieval am Arbeitsplatz
Sprachverarbeitung in Information und Dokumentation, Jahrestagung der Gesellschaft für Linguistische Datenverarbeitung (GLDV) in Kooperation mit der Fachgruppe 3 "Natürlichsprachliche Systeme" im FA 1.2 der Gesellschaft für Informatik (GI)
The Knowledge Engineering Review
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
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Nowadays, we witness the advent of a new era in information technology as an enabler for an often proclaimed “knowledged society”. Many academicians – computer scientists, computational linguists and others – but also researchers in corporate settings investigate the use of “knowledge” as a means for intelligent information access in enterprises. Ontologies are commonly considered a formal means for modelling and instantiating conceptual structures upon which implicit knowledge can be inferred. In an industrial context, however, domain-specific conceptual information is only one corporate information resource. Relevant business information comprises facts on goods, projects, experts, customers, competitors and many other information bits as they are stored in enterprise information systems. A great wish of many executives is, however, to gain an integrated view on these assets and their relationships, thus deriving new insights relevant for their business. To achieve this goal, ontologies can help with respect to semantic data integration. A problem is, however, that the construction and maintenance of ontologies is expensive. Another problem is that the business data are usually stored redundantly in different heterogeneous data repositories and the connectivity – a prerequisite for intelligence – is not explicit. The approach discussed in the remainder will show how a mix of information extraction and classification methods can be used to automatically set-up and update a network of business objects serving as a corporate memory index. The latter represents a rich semantic access structure for filtering and individualizing the retrieval of relevant business information.