Web Taxonomy Fusion using Topic Maps-driven Ontological Concepts and Relationships

Since most of theWeb taxonomies and catalogs are organized in conceptual hierarchies, taxonomy fusion can be viewed as a specialized case of hierarchical ontology coalition in real-world applications. Hence, different kinds of semantic information can be further extracted to facilitate Web taxonomy fusion, such as intra-ontological concepts and interontological relationships. This paper proposes approaches to effectively improve the accuracy of Web taxonomy fusion by using a taxonomy fusion model based on the ontological concepts and relationships of Topic Maps. Specifically, a novel fusion model based on inter-ontological mapping as well as intra-topic concept is presented to outperform a Näıve Bayes (NB) classifier and a Support Vector Machine (SVM) by 20% to 30% in F1 measure over real-world Web taxonomies.