Conclusion

We have presented a novel approach to automatically acquire concept hierarchies from domain-specific texts. In addition, we have compared our approach with a hierarchical agglomerative clustering algorithm as well as with Bi-Section-KMeans and found that our approach produces better results on the two datasets considered. We have further examined different information measures to weight the significance of an attribute/object pair and concluded that the conditional probability works well compared to other more elaborate information measures. We have also analyzed the impact of a smoothing technique in order to cope with data sparseness and found that it doesn't improve the results of the FCA-based approach. Further, we have highlighted advantages and disadvantages of the three approaches. Though our approach is fully automatic, it is important to mention that we do not believe in fully automatic ontology construction without any user involvement. In this sense, in the future we will explore how users can be involved in the process by presenting him/her ontological relations for validation in such way that the necessary user feedback is kept at a minimum. On the other hand, before involving users in a semi-automatic way it is necessary to clarify how good a certain approach works per se. The research presented in this paper has had this aim. Furthermore, we have also proposed a systematic way of evaluating ontologies by comparing them to a certain human-modeled ontology. In this sense our aim has also been to establish a baseline for further research.