Improving the Accuracy of Ontology Alignment through Ensemble Fuzzy Clustering
نویسندگان
چکیده
Automatic ontology alignment tools perform matching between the concepts of two ontologies and provide a similarity measure for each pair of aligned concepts. However, none of the existing tools are perfect and multiple alignment tools produce varying similarity measures for a certain alignment. Also, the similarity measures provided by an alignment may not be helpful enough for indicating the degree of reliability. While using a random alignment tool we noticed that some quality alignments are given medium or even low similarity measures, and that causes the user ignoring those alignments. In this study we have proposed an ensemble model of ontology alignment that aggregates multiple alignment tools with the help of Fuzzy C Means clustering and Type 2 Fuzzy Membership Functions. We have shown that our approach helps the user to choose the best alignment results which has not been obtained by any other alignment tools we experimented with.
منابع مشابه
Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering
Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...
متن کاملCentralized Clustering Method To Increase Accuracy In Ontology Matching Systems
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory con...
متن کاملA new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...
متن کاملخوشهبندی اسناد مبتنی بر آنتولوژی و رویکرد فازی
Data mining, also known as knowledge discovery in database, is the process to discover unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups. The most important step...
متن کاملImproving Imbalanced data classification accuracy by using Fuzzy Similarity Measure and subtractive clustering
Classification is an one of the important parts of data mining and knowledge discovery. In most cases, the data that is utilized to used to training the clusters is not well distributed. This inappropriate distribution occurs when one class has a large number of samples but while the number of other class samples is naturally inherently low. In general, the methods of solving this kind of prob...
متن کامل