@INPROCEEDINGS{ZhaoAlghamdiEtAl19a,
AUTHOR = {Zhao, Y. and Alghamdi, G. and Schmidt, R. A. and Feng, H. and Stoilos, G. and Juric, D. and Khodadadi, M.},
YEAR = {2019},
TITLE = {Tracking Logical Difference in Large-Scale Ontologies: A Forgetting-Based Approach},
EDITOR = {{Van Hentenryck}, P. and Zhou, Z.-H.},
BOOKTITLE = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-2019)},
PUBLISHER = {AAAI Press},
PAGES = {3116--3124},
ISBN = {978-1-57735-809-1},
DOI = {10.1609/aaai.v33i01.33013116},
URL = {http://www.cs.man.ac.uk/~schmidt/publications/ZhaoAlghamdiEtAl19a.html},
ABSTRACT = {This paper explores how the logical difference between two
ontologies can be tracked using a forgetting-based or uniform
interpolation (UI)-based approach. The idea is that rather than
computing all entailments of one ontology not entailed by
the other ontology, which would be computationally infeasible, 
only the strongest entailments not entailed in the other
ontology are computed. To overcome drawbacks of existing 
forgetting/uniform interpolation tools we introduce a new
forgetting method designed for the task of computing the
logical difference between different versions of large-scale
ontologies. The method is sound and terminating, and can
compute uniform interpolants for ALC-ontologies as large
as SNOMED CT and NCIt. Our evaluation shows that the
method can achieve considerably better success rates (>90\%)
and provides a feasible approach to computing the logical 
difference in large-scale ontologies, as a case study on different
versions of SNOMED CT and NCIt ontologies shows.
},
}