Noun phrases extraction using shallow parsing with C4.5 decision tree algorithm for Indonesian Language ontology building

Ontology describes a set of concept or entity and each relation. Ontology as knowledge representation usually has a large structure because it can cover a wide area topics. Ontology building process is divided into two subprocesses, those are term extraction and relation formation. Term extraction in ontology building is done for extracting concept or entity before each relation is obtained. Main objective in this research is to extract noun phrases using shallow parsing algorithm based on C4.5 decision tree as candidate concept or term for ontology building process in Indonesian Text. One of the advantages of using shallow parsing is it can recover syntactic information efficiently and reliably from unrestricted text. For our dataset, we use Indonesian Language online newspapers for one month. Based on our experiments, it concludes that our proposed method can perform well for Indonesian Language noun phrase identification with average F-score 84.63%.