Journals Proceedings

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 data and it produces a general DAG. The third stage is an analysis process using some rules applied to clustered DAGs and the general DAG to determine connector nodes. A connector node is located in a clustered DAG and it has a relation (edge) to other node in another clustered DAG. It causes cluster of DAGs to be a union graph called an Ontology Model which represent knowledge of the text domain. Data in this works consist of simulation data using a small number of key words from natural science. The ontology model resulted is evaluated manually and it shows that the knowledge of text can be represented visually. The experiment of ontology development still has some challenges to be improved.