A Personalised ontology framework for interpreting discovered knowledge in text information
Md Abul Bashar · Queensland University of Technology · 2017
Many text mining techniques have been developed to discover useful knowledge from text data. However, they produce a large body of knowledge without semantic information, which causes the meanings of the discovered knowledge difficult to interpret. Thus, the utilisation of the discovered knowledge is hindered. We propose to mine a personalised ontology to improve the semantic interpretation of discovered knowledge. The personalised ontology is structured by combining the discovered knowledge with a knowledge-base ontology and the data context. The challenging issues in the mining process are addressed. Both qualitative and quantitative evaluations confirm the merits of the proposed framework.