Table 4.

Summary of Ontology Learning: Challenges and Future Directions

	Challenge	Proposed Solution
1	Diversity of formatted data, multi-lingual data	Novel approaches to integrate and harmonize data Cross-language ontologies advanced algorithms for ontology learning
2	Lack of automatic ontology validation, faulty ontologies	Use of social web, collaborative tagging and folksonomy Use of search engines for answer validation
3	Scalability of ontology learning techniques	Increase in research to accommodate larger datasets Arrangement of community challenges by governing bodies to increase the research scale of ontology learning techniques
4	Requirement of human intervention for better quality of learned ontologies	Need of automatic post processing techniques Integrate post processing framework with ontology learning framework to boost the quality of ontology Use of research in the fields of crowdsourcing and human-based computation games
5	Lack of heavy weight ontologies	Strengthen axiom learning algorithms