tailieunhanh - Lecture Notes in Computer Science- P106

Lecture Notes in Computer Science- P106:This year, we received about 170 submissions to ICWL 2008. There were a total of 52 full papers, representing an acceptance rate of about 30%, plus one invited paper accepted for inclusion in this LNCS proceedings. The authors of these accepted papers | 514 S. Repp S. Linckels and C. Meinel The algorithm works as follows We compute for each identified concept rule its hit-rate h . its frequency of occurrence inside the leaning object. Only the concepts roles with the maximum or dth maximum hit-rate compared to the hit-rate in the other learning objects are used as metadata. . the concept Topology has the following hit-rate for the five learning objects LOi to LO5 LO1 LO2 LO3 LO4 LO5 h 0 4 3 7 This means that the concept Topology was not mentioned in LO1 but 4 times in LO2 3 times in LO3 etc. We now introduce the rank d of the learning object . the hit-rate of a concept role. For a given rank . d 1 the concept Topology is relevant only in the learning object LO4 because it has the highest hit-rate. For d 2 the concept is associated to the learning objects LO4 and LO2 . the two learning ob jects with the highest hit-rate. Semantic Annotation Generation The semantic annotation of a given learning ob ject is the conjunction of the mappings of each relevant word in the source data written m LO rankd p wi e y LO. _ i 1 where m is the number of relevant words in the data source and d the rank of the mapped concept role. The result of this process is a valid DL description similar to that shown in figure . In the current state of the algorithm we do not consider complex role imbrications . JR. A n 3S. B n A where A B are atomic concepts and R S are roles. We also try to use a very simple DL . negations A are not considered. One of the advantages of using DL is that it can be serialized in a machine readable form without losing any of its details. Logical inference is possible when using these annotations. The example shows the OWL serialization for the following DL-concept description LO1 IPAddress n 3isComposedOf. Host-ID n Network-ID defining a concept name LO1 for the concept description saying that an IP address is composed of a host identifier and a network identifier. 4 Evaluation .