Business Information Systems: 11th International Conference, by Witold Abramowicz

By Witold Abramowicz

This ebook includes the refereed court cases of the eleventh foreign convention on company info platforms, BIS 2008, held in Innsbruck, Austria, in may perhaps 2008. The forty-one revised complete papers have been conscientiously reviewed and chosen inclusion within the booklet. The contributions conceal learn tendencies in addition to present achievements and leading edge advancements within the zone of recent company info platforms. they're grouped in sections on company approach administration, provider discovery and composition, ontologies, info retrieval, firm source making plans, interoperability, mobility and contexts, wikis and folksonomies, and principles and semantic queries.

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Extra info for Business Information Systems: 11th International Conference, BIS 2008, Innsbruck, Austria, May 5-7, 2008, Proceedings (Lecture Notes in Business Information Processing)

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LNCS, vol. 2113, pp. 62–71. Springer, Heidelberg (2001) 13. : Time Annotation Guidelines For Less Commonly Taught Languages (2006) 14. : Robust temporal processing of news. In: ACL 2000: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, Morristown, NJ, USA, pp. 69–76 (2000) 15. : How many relevances in information retrieval? Interacting with Computers 10(3), 303–320 (1998) 16. : Improving information retrieval effectiveness by using domain knowledge stored in ontologies.

For other granularities, these are granules which contain this day. 2 (1) Preliminary Results Before the development of the new indexing method could have been started, basic assumption must have been verified. It was assumed that syntactically similar documents should have similar temporal indexes. Language Model Based Temporal Information Indexing 29 In order to compare documents syntactically Vector Space Model (VSM) was employed. Each document was represented as a vector of terms di = (w1 , .

The Levenshtein metric is a text similarity metric which calculates the distance between two words. More specifically, it counts how many letters have to be replaced, deleted or inserted to transform one word into the other [18]. Dividing this sum by the total number of letters in the word, gives us the Levenshtein metric. It is a valuable technique to verify the similarities of two tags. In order to calculate the distance, first all possible tag pairs have to be 40 C. Van Damme, T. Coenen, and E.

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