By S. Handschuh, S. Staab
The Semantic net goals at computing device brokers that thrive on explicitly unique semantics of content material on the way to seek, clear out, condense, or negotiate wisdom for his or her human clients. A center know-how for making the Semantic net ensue, but additionally to leverage program components like wisdom administration and E-Business, is the sector of Semantic Annotation, which turns human-understandable content material right into a computing device comprehensible shape. This publication studies at the large diversity of applied sciences which are used to accomplish this translation and nourish third millennium functions. The ebook begins with a survey of the oldest semantic annotations, viz. indexing of courses in libraries. It maintains with a number of ideas for the categorical development of semantic annotations, together with techniques for collaboration and Semantic internet metadata. one of many significant skill for making improvements to the semantic annotation activity is info extraction and lots more and plenty should be discovered from the semantic tagging of linguistic corpora. particularly, info extraction is gaining prominence for automating the previously only handbook annotation activity - a minimum of to a couple extent.An very important subclass of knowledge extraction initiatives is the goal-oriented extraction of content material from HTML and / or XML assets.
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Aberer and L. Liu, editors, ODBASE-2002 — Ontologies, Databases and Applications ofSEmantics. Irvine, CA, USA, Oct. 29-31, 2002, LNCS, pages 1025-1222. Springer, 2002.  M. Vargas-Vera, E. Motta, J. Domingue, M. Lanzoni, A. Stutt, and F. Ciravegna. MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup. In EKAW02, 13th International Conference on Knowledge Engineering and Knowledge Management, LNCS/LNAI 2473, pages 379-391, Sigiienza, Spain, October 2002. Springer.  M.
Motta, J. Domingue, M. Lanzoni, A. Stutt, and F. Ciravegna. MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup. In EKAW02, 13th International Conference on Knowledge Engineering and Knowledge Management, LNCS/LNAI 2473, pages 379-391, Sigiienza, Spain, October 2002. Springer.  M. Vargas-Vera, E. Motta, J. Domingue, S. Buckingham Shum, and M. Lanzoni. Knowledge Extraction by using an Ontology-based Annotation Tool. In Proceedings of the Knowledge Markup and Semantic Annotation Workshop 2001 (at K-CAP 2001), pages 5-12, Victoria, BC, Canada, October 2001.
Attributes which resemble the column name are preselected (cf. dialog #la in Figure 8). If the user clicks OK, database concept and instance checks are performed and the new generic instance is created. Generic instances will appear with a database symbol in their icon. Figure 8: Screenshot of Providing Deep Annotation with OntoMat Create Generic Attribute Instances: In order to create a generic attribute instance the user simply drops the server-side markup into the corresponding table entry (cf.