OntoDNA: Ontology Alignment Results for OAEI 2007

OntoDNA is an automated ontology mapping and merging system that utilizes unsupervised data mining methods, comprising of Formal Concept analysis (FCA), Self-Organizing map (SOM) and K-means incorporated with lexical similarity, namely Levenshtein edit distance. The unsupervised data mining methods are used to resolve structural and semantic heterogeneities between ontologies, meanwhile lexical similarity is used to resolve lexical heterogeneity between ontologies. OntoDNA generates a merged ontology in concept lattice that enables visualization of the concept space based on formal context. This paper briefly describes the OntoDNA system and discusses the obtained alignment results on some of the OAEI 2007 dataset. The paper also presents strengths and weaknesses of our system and the method to improve the current approach. 1 Presentation of the system

• Type: Conference
• Year: 2007
• Where: SEMWEB
• Authors: Ching-Chieh Kiu, Chien-Sing Lee
• Keywords: Data Mining Methods, Lexical Similarity, SEMWEB 2007, Unsupervised Data Mining