Domain Ontology Learning using Link Grammar Parser and WordNet

The problem of knowledge discovery that comes down to information pertinence evaluation still stay unsolved because of absence of practical effective methods and means of ontology learning. Despite active development of natural language processing tools, they mostly reach the level of semantics, named entity recognition and sentiment analysis but not pragmatics. On the other hand, ontology is the only instrument, “measuring ruler” to compare and estimate the usefulness of information for some particular user, which knowledge could be represented by such ontology as his hierarchical task network (HTN). The need to build separate HTN ontology for each user puts on the agenda the task of design of the automated ontology learning from text. With aim to solve this task the system of automated and semi-automated ontology learning from text had been developed using Carnegie Mellon Link Grammar Parser and WordNet API. Two approaches to distinguish semantic relations in natural language text (NLT) sentences were adopted: analysis of the sentence constituent trees – for explicit relations recognition and Naïve Bayes supervised learning – for recognition implicit semantic relations which need not only verb phrase but other parts of sentence due to its ambiguity. Developed approach was implemented in the Java desktop application using OWL API and Protégé-OWL API. Experimental results were compared to expert analysis and had shown good recognition reliability.