By P. Adjiman, P. Chatalic, F. Goasdoué, M. -C. Rousset, L. Simon (auth.), François Fages, Sylvain Soliman (eds.)
This booklet constitutes the refereed complaints of the 3rd foreign Workshop on rules and perform of Semantic net Reasoning, PPSWR 2005, held in Dagstuhl fort, Germany in September 2005.
The 12 revised complete papers offered including three invited contributions have been conscientiously reviewed and chosen for inclusion within the booklet. the key facets of semantic net study are addressed within the papers, specifically semantic internet architectures, language matters, and formal reasoning equipment. The advances are investigated within the context of latest layout rules and tough functions.
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Additional resources for Principles and Practice of Semantic Web Reasoning: Third International Workshop, PPSWR 2005, Dagstuhl Castle, Germany, September 11-16, 2005 Proceedings
Each site publishes the events it emits, together with the event’s recipients, on a ‘blackboard’ which is repeatedly queried by the potential recipient sites. Such queries are called continuous. With the push model, event can be sent as ‘data streams’ . Continuous queries [22,1,17,18], data streams , and event queries [5,2] require speciﬁc query evaluation methods. 3 Semantics Thesis 7 (Declarative Semantics). Logic languages for the Semantic Web, except reactive rule languages, should have declarative semantics deﬁned as ‘Tarski-style model theories’.
Similar strings will have a high proportion of n-grams in common. Edit distance is deﬁned as the number of deletions, insertions, or substitutions required to transforming one string into the other. The greater the edit distance, the more diﬀerent the strings are. The linguistic algorithm computes the similarity of the terms by comparing the lists of words of which the terms are composed. Similar terms have high proportion of words in common in the lists. A porter stemming algorithm is employed to each word.
Lambrix and H. Tan instance corpora general dictionaries domain thesauri alignment algorithm s o u r c e o n t o l o g i e s matcher matcher combination filter suggestions user a l i g n m e n t s conflict checker accepted suggestions Fig. 1. A general alignment strategy source ontologies alignments merging computation checker computed ontology user merged ontology Fig. 2. A general merging algorithm – Instance-based strategies. In some cases instances are available directly or can be obtained.