@INPROCEEDINGS{ZhaoSchmidt18a,
AUTHOR = {Zhao, Y. and Schmidt, R. A.},
YEAR = {2018},
TITLE = {FAME: An Automated Tool for Semantic Forgetting in Expressive Description Logics},
EDITOR = {Galmiche, D. and Schulz, S. and Sebastiani, R.},
BOOKTITLE = {Automated Reasoning (IJCAR 2018)},
SERIES = {Lecture Notes in Artificial Intelligence},
VOLUME = {10900},
PUBLISHER = {Springer},
PAGES = {19--27},
DOI = {https://doi.org/10.1007/978-3-319-94205-6_2},
URL = {http://www.cs.man.ac.uk/~schmidt/publications/ZhaoSchmidt18a.html},
ABSTRACT = {In this paper, we describe a high-performance reasoning tool, called
\textsc{Fame}, for semantic forgetting in expressive description logics.
Forgetting is a non-standard reasoning service that seeks to create
restricted views of ontologies by eliminating concept and role names
from ontologies in a way such that all logical consequences up to
the remaining signature are preserved.
\textsc{Fame} is a Java-based implementation of an Ackermann-based
method for forgetting concept and role names from ontologies
expressible in the description logic $\mathcal{ALCOIH}$.
$\mathcal{ALCOIH}$ is the extension of 
the basic description logic $\mathcal{ALC}$ with nominals, inverse roles and
role inclusions.
\textsc{Fame} can be used as a standalone tool or a Java library for
forgetting or related tasks.
Results of an evaluation of \textsc{Fame} on a corpus of 396 biomedical
ontologies have shown that:
(i) in more than 90\% of the test cases \textsc{Fame} was successful
(i.e., eliminated all specified concept and role names) and (ii)
the elimination was done within one second in more than 70\% of the
successful cases.
},
}