A structured ontology construction by using data clustering and pattern tree mining

Ontology is used to express the concepts of domain knowledge. It can provide a common representation for different agents to share and communicate knowledge for conducting unified opinions. Nowadays ontology construction method is divided into man-made and machine-made mechanisms. The former constructs the ontology topology by domain expert. Generally the constructed ontology can fit human expectation but it needs more development time to construct the whole structure. The latter uses semi-automatic or automatic methods, such as statistic or machine learning, to build the ontology. The efficient ontology construction is the main advantage for machine-made method. However, the advantage is that it is easily influenced by the category and type of domain concepts to generate unbalanced or skewed ontology topology. This will increase the time complexity to search and retrieve the concept from the constructed ontology structure. The situation worsens from being unable to use the ontology properly. An important problem is constructing a reasonable and balanced ontology topology systematically and automatically. This paper proposes a structured ontology construction based on data clustering and pattern tree mining. The construction method uses data clustering and formal concept analysis to group similar documents for constructing ontology trees of each group individually. Then the method uses pattern tree mining to build an integrated ontology topology from partial ontology trees.