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Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE. 43rd Annual Meeting of the Association for Computational Linguistics (ACL2005), Ann Arbour, Michigan, pp. 491-498. Maedche, A. (2002). Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Norwell, MA. Meadche, A. & Staab, S. (2001). Ontology learning for the semantic Web. The IEEE Journal on Intelligent Systems, Vol. 16, No. 2, pp. 72-79. L. (1995). Use of context in explanations systems. 8 No. 4, pp.

1993). Towards Principles for the Design of Ontologies used for Knowledge sharing. In N. Guarino & R. Poli (Eds), Formal Ontology in Conceptual Analysis and Knowledge Representation. Deventer, the Netherlands: Kluwer Academic Publishers. Brézillon, P. (1999). Context in problem solving: A survey. The Knowledge Engineering Review, Volume: 14, Issue: 1, Pages: 1-34. Han, H. & Elmasri, R (2000). Architecture of WebOntEx: A system for automatic extraction of ontologies from the Web. WCM 2000. Brézillon, P.

We can thus characterize three different approaches to the implementation of reasoning services. The first can be referred to as limited + complete, and includes systems that are designed by restricting the set of constructs in such a way that subsumption would be computed efficiently, possibly in polynomial time. The second approach can be denoted as expressive + incomplete, since the idea is to provide both an expressive language and efficient reasoning. The drawback is, however, that reasoning algorithms turn out to be incomplete in these systems.

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Algorithms for Delta Compression and Remote File Synchronization by Suel Memon


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