Information Systems Engineering in Complex Environments by Selmin Nurcan & Elias Pimenidis

Author:Selmin Nurcan & Elias Pimenidis
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Advertisements and Ranking: We have annotated a subset of the SAWSDL-TC3 service advertisements with our annotation paths method. The SAWSDL-TC3 contains relevance grades for each advertisement and each request. The relevance grades are 0 (not relevant), 1 (partially relevant), 2 (relevant) and 3 (highly relevant). We have annotated 5 random advertisements of each relevance grade. The selected advertisements, their defined relevance grades and the ranking computed by our simple matcher (SAPM-WS) are shown in Table 1. The 3 best ranked services of our approach also have the highest relevance grade of 3. The other two services with a relevance grade of 3 are the - rank 6 in our result and the (rank 7). The advertisement bookpersoncreditcardaccount_price_service has been ranked to position 4 with a confidence value of 0.9412, while it has an expert rating of 2. The reason for this is that the input of the advertisement consists of a , a and a type. While the type consists of several other subtypes, and are single types and have not as much weight as the book type with its 6 sub elements in our basic aggregation function. Finally, the advertisement sciencefictionbookuser_price_service, ranked to rank 14 has a confidence value of 0.5000 with a predefined rating of 2. This low rating results from the concept, which is only a sub concept of the concept. Furthermore, there is additionally a concept in the input of the advertisement, which cannot be found in the request. Overall, we suppose that our simple matcher that operates only on annotation paths with a very simple aggregation method and static parameters already achieves good results. Tuning and more sophisticated aggregation and weighting methods will help achieve even better results.Table 1.Scenario 1: SAPM-WS

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