![](bg9.png)
REFERENCES
Alsubait, T., Parsia, B., and Sattler, U. (2014). Measur-
ing conceptual similarity in ontologies: how bad is
a cheap measure? In Informal Proceedings of the
27th International Workshop on Description Logics,
Vienna, Austria, July 17-20, 2014., pages 365–377.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D.,
Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K.,
Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P.,
Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese,
J. C., Richardson, J. E., Ringwald, M., Rubin, G. M.,
and Sherlock, G. (2000). Gene Ontology: tool for the
unification of biology. Nature Genetics, 25(1):25–29.
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D.,
and Patel-Schneider, P. F. (2010). The Description
Logic Handbook: Theory, Implementation and Appli-
cations. Cambridge University Press, New York, NY,
USA, 2nd edition.
Baader, F. and Narendran, P. (2001). Unification of con-
cept terms in description logics. Journal of Symbolic
Computation, 31(3):277 – 305.
Bernstein, A., Kaufmann, E., B
¨
urki, C., and Klein, M.
(2005). How Similar Is It? Towards Personalized
Similarity Measures in Ontologies, pages 1347–1366.
Physica-Verlag HD, Heidelberg.
Caviedes, J. E. and Cimino, J. J. (2004). Towards the devel-
opment of a conceptual distance metric for the UMLS.
Journal of Biomedical Informatics, 37(2):77–85.
D’Amato, C., Fanizzi, N., and Esposito, F. (2009). A se-
mantic similarity measure for expressive description
logics. In CoRR, abs/0911.5043.
D’Amato, C., Staab, S., and Fanizzi, N. (2008). On the in-
fluence of description logics ontologies on conceptual
similarity. In Proceedings of Knowledge Engineering:
Practice and Patterns, pages 48–63.
Euzenat, J. and Valtchev, P. (2004). Similarity-based on-
tology alignment in OWL-lite. In de M
´
antaras, R. L.
and Saitta, L., editors, Proceedings of the 16th Euro-
pean Conference on Artificial Intelligence (ECAI-04),
pages 333–337. IOS Press.
Ge, J. and Qiu, Y. (2008). Concept similarity matching
based on semantic distance. In Proceedings of the
4th International Conference on Semantics, Knowl-
edge and Grid, pages 380–383.
Jaccard, P. (1901).
´
Etude comparative de la distribution flo-
rale dans une portion des alpeset des jura. Bulletin de
la Societe Vaudoise des Sciences Naturellese, 37:547–
579.
Lehmann, K. and Turhan, A.-Y. (2012). A framework
for semantic-based similarity measures for ELH -
concepts. In del Cerro, L. F., Herzig, A., and Mengin,
J., editors, JELIA, volume 7519 of Lecture Notes in
Computer Science, pages 307–319. Springer.
Patwardhan, S. (2006). Using wordnet-based context vec-
tors to estimate the semantic relatedness of concepts.
In Proceedings of the EACL 2006 Workshop Making
Sense of Sense-bringing Computational Linguistics
and Psycholinguistics Together, volume 1501, pages
1–8.
Pedersen, T., Pakhomov, S. V., Patwardhan, S., and Chute,
C. G. (2007). Measures of semantic similarity and
relatedness in the biomedical domain. Journal of
Biomedical Informatics, 40(3):288 – 299.
Racharak, T. and Suntisrivaraporn, B. (2015). Similar-
ity measures for FL
0
concept descriptions from an
automata-theoretic point of view. In Proceedings of
the 6th International Conference of Information and
Communication Technology for Embedded Systems
(IC-ICTES), pages 1–6.
Racharak, T., Suntisrivaraporn, B., and Tojo, S. (2016a).
Identifying an Agent’s Preferences Toward Similar-
ity Measures in Description Logics, pages 201–208.
Springer International Publishing, Cham.
Racharak, T., Suntisrivaraporn, B., and Tojo, S. (2016b).
sim
π
: A concept similarity measure under an agent’s
preferences in description logic ELH . In Proceedings
of the 8th International Conference on Agents and Ar-
tificial Intelligence, pages 480–487.
Racharak, T., Suntisrivaraporn, B., and Tojo, S. (2017a).
Personalizing a concept similarity measure in the de-
scription logic ELH with preference profile. Journal
of Computing and Informatics (to appear).
Racharak, T., Tojo, S., Hung, N. D., and Boonkwan, P.
(2016c). Argument-based logic programming for
analogical reasoning. In New Frontiers in Artifi-
cial Intelligence - JSAI-isAI 2016 Workshops, LENLS,
HAT-MASH, AI-Biz, JURISIN and SKL, Kanagawa,
Japan, November 14-16, 2016, Revised Selected Pa-
pers, pages 253–269.
Racharak, T., Tojo, S., Hung, N. D., and Boonkwan, P.
(2017b). Combining answer set programming with
description logics for analogical reasoning under an
agent’s preferences. In Advances in Artificial Intelli-
gence: From Theory to Practice - 30th International
Conference on Industrial Engineering and Other Ap-
plications of Applied Intelligent Systems, IEA/AIE
2017, Arras, France, June 27-30, 2017, Proceedings,
Part II, pages 306–316.
Rada, R., Mili, H., Bicknell, E., and Blettner, M. (1989).
Development and application of a metric on semantic
nets. IEEE Transactions on Systems, Man, and Cyber-
netics, 19(1):17–30.
Raha, S., Hossain, A., and Ghosh, S. (2008). Similarity
based approximate reasoning: fuzzy control. Journal
of Applied Logic, 6(1):47 – 71.
Resnik, P. (1995). Using information content to evaluate se-
mantic similarity in a taxonomy. In Proceedings of the
14th International Joint Conference on Artificial In-
telligence - Volume 1, IJCAI’95, pages 448–453, San
Francisco, CA, USA. Morgan Kaufmann Publishers
Inc.
Sch
¨
utze, H. (1998). Automatic word sense discrimination.
Computational Linguistics, 24(1):97–123.
Sessa, M. I. (2002). Approximate reasoning by similarity-
based SLD resolution. Theoretical Computer Science,
275(12):389 – 426.
Suntisrivaraporn, B. (2013). A similarity measure for the
description logic el with unfoldable terminologies. In
INCoS, pages 408–413.
Concept Similarity under the Agent’s Preferences for the Description Logic FL
0
with Unfoldable TBox
209