Ontology Matching using BabelNet Dictionary and Word Sense Disambiguation Algorithms

<p>Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu &amp; Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu &amp; Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).</p>

SINONIM DAN WORD SENSE DISAMBIGUATION UNTUK MELENGKAPI DETEKTOR PLAGIAT DOKUMEN TUGAS AKHIR

Plagiarism can be categorized into several levels: carbon copy, the addition of words, word substitutions, changing active into passive sentences, and paraphrase. In this research, the detection is only performed by local similarity assessment method. This research is categorized into 3 major processes: preprocessing, candidate determination, and calculation of similarity. In preprocessing, extraction and conversion of a PDF file into XML is performed. Stopword removal and stemming are also performed in this process. For Candidates determination, the process used VSM (Vector Space Model) algorithm using Lucene.NET. It will then calculate the similarity values of the candidates. Similarity values that meet the threshold will be processed in the third stage. The next process is detecting plagiarism at the level of carbon copy. The plagiarism of the substitution level will be determined by finding synonymous with Lesk algorithm and utilizing WordNet as a language dictionary. Lesk notice the words around it, before doing the search process is synonymous with Lesk, performed first sentence extractor. From this experiment, it is concluded that the determination of synonyms using WordNet and Lesk algorithm does not seem to increase its similarity value role. This is due to the difficulty of finding plagiarism by just substituting words. However, plagiarism at the level of carbon copy can be handled with the help of sentence matching.