Abstract: “Recently, development of methods in extracting knowledge from a text collection is still explored. In this work, the proposed approach utilize important words or key words that represent a domain of text. The key words may have relations among them and the relational keywords in the text domain can be organized become an ontology model as a domain knowledge. The proposed method for forming knowledge represented the text consists of three stages process. First, Vector Space Model (VSM) of key words from text is clustered using bottom-up approach and each clustered data is categorized to be an input of structure learning in a Bayesian network concept. The next stage, structure development of each clustered data using Markov Chain Monte Carlo (MCMC) method such that key words as nodes are related each other as in DAG (directed acyclic graph) form. The result of structure learning process of each cluster produces a clustered DAG. The same learning process is also applied to the original da”

Keywords: bottom-up clustering, MCMC, Connector Node, Ontology