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

By Witold Abramowicz

This booklet comprises the refereed complaints of the eleventh foreign convention on enterprise details structures, BIS 2008, held in Innsbruck, Austria, in may well 2008. The forty-one revised complete papers have been rigorously reviewed and chosen inclusion within the publication. The contributions conceal study tendencies in addition to present achievements and leading edge advancements within the sector of contemporary company info structures. they're grouped in sections on enterprise approach administration, provider discovery and composition, ontologies, details retrieval, firm source making plans, interoperability, mobility and contexts, wikis and folksonomies, and ideas 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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Time model will vary among documents related to different time periods. 2 Related Work The problem of searching for information using temporal criteria is vital, especially in domains where time aspect plays a crucial role (for sample applications see [2,11]). In financial markets analysis, which is in the center of our interest, users combine: transaction data, financial statements and other textual infor- Language Model Based Temporal Information Indexing 27 mation (including news stories) to form complex view on a market situation.

Users can enter any words that enter their mind. This makes them active participators in creating new tags. Aggregating this user created meta data leads to a flat, bottom-up taxonomy, also known as a folksonomy. Despite the strengths, tagging has its weaknesses: no conceptual meaning or hierarchical relations are added to the tags. As a consequence, tags have no synonyms or homonyms. Furthermore, specialized as well as general tags can be used to annotate the same resource [7,8]. g. cluster tags as on Delicious) [8] and/or (2) trying to generate more information on the tags by employing text mining, statistical techniques and asking additional feedback from the community.

Turning a Corporate Folksonomy into a Lightweight Corporate Ontology 41 Social Network Analysis. These techniques make clusters of people based on their shared objects and or tags. In [10], these techniques are suggested to analyze the ontology of a community. This could be useful to a company, to analyze the ontology of a team. Transitive Reduction. In [16] the authors remove the roots that are logically above the parent nodes. However, we believe transitive reduction, a technique from graph theory, is far more interesting.

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