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Dewi Wardani
Universitas Sebelas Maret
Computer Science & IT Electrical & Electronics Engineering

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The Utilization of Ontology to Support The Results of Association Rule Apriori Dewi Wardani; Achmad Khusyaini
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.047 KB) | DOI: 10.11591/eecsi.v5.1642

Abstract

Association rule is one of the data mining techniques to find associative combinations of items. There are several algorithms including Apriori, FP - Growth, and CT-Pro. One of the advantages of the Apriori algorithm is that it produces many rules. To improve its result, one of the methods is by using the semantic web technology. In this work, we propose how the hierarchical type of ontology can be utilized by the Apriori algorithm to improve the results. The Apriori with ontology implements the IR which is a parameter to determine the degree of association between combinations of items in a dataset. The series of experiments show that the proposed idea can improve the results compare to the default Apriori algorithm
Evaluating The Semantic Mapping Dewi Wardani
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.514 KB) | DOI: 10.11591/eecsi.v5.1648

Abstract

Along the increasing of the importance of links in the network of data, they should be considered more in the mapping relational to graph model. Semantic abstraction gaps often occur during the mapping process where the link in the real world is mapped as a node in a graph model. This paper focused on evaluating the result of mapping and converting without losing the semantics. We propose the evaluation of our approach by using schema.org as the semantic standard. The experiments in three data sets show that the semantic mapping approach is pretty effective. We obtain quite good score matching without considering the gap index (the average is 0.6922) and with considering the gap index (the average is 0.5264) and the average precision score, 0.7042, is pretty good too.
Co-Authors Achmad Khusyaini