Access the full text.
Sign up today, get DeepDyve free for 14 days.
Jan Nößner, Mathias Niepert (2010)
CODI: Combinatorial Optimization for Data Integration: results for OAEI 2011
Natasha Noy, M. Musen (2003)
The PROMPT suite: interactive tools for ontology merging and mappingInt. J. Hum. Comput. Stud., 59
D. McGuinness, R. Fikes, James Rice, S. Wilder (2000)
An Environment for Merging and Testing Large Ontologies
Zoulfa Jerroudi, J. Ziegler (2008)
iMERGE : Interactive Ontology Merging
E Jiménez-Ruiz, B Cuenca-Grau (2011)
LogMap: logic-based and scalable ontology matching. The semantic web-ISWC 2011Lect Notes Comput Sci, 7031
M. Horridge, S. Bechhofer (2011)
The OWL API: A Java API for OWL ontologiesSemantic Web, 2
M. Fahad, N. Moalla, A. Bouras (2012)
Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging systemJournal of Intelligent Information Systems, 39
D. Dou, D. McDermott (2004)
Ontology translation by ontology merging and automated reasoning
Mike Dean, A. Schreiber, S. Bechofer, F. Harmelen, J. Hendler, Ian Horrocks, D. MacGuinness, P. Patel-Schneider, L. Stein (2004)
OWL Web Ontology Language - Reference
Konstantinos Kotis, A. Katasonov, J. Leino (2012)
AUTOMSv2 results for OAEI 2012
J. Bruijn, M. Ehrig, C. Feier, Francisco Martíns‐Recuerda, F. Scharffe, M. Weiten (2006)
Ontology Mediation, Merging, and Aligning
J. Euzenat, Malgorzata Mochól, P. Shvaiko, H. Stuckenschmidt, Ondřej Šváb, V. Svátek, W. Hage, Mikalai Yatskevich (2007)
Results of the Ontology Alignment Evaluation Initiative
I. Cruz, F. Antonelli, Cosmin Stroe (2009)
AgreementMaker: Efficient Matching for Large Real-World Schemas and OntologiesProc. VLDB Endow., 2
E Jimenez-Ruiz, B Cuenca Grau, U Sattler, T Schneider, R Berlanga (2008)
Safe and economic re-use of ontologies: a logic-based methodology and tool support, ESWC 2008LNCS, 5021
Ahmed Alasoud, V. Haarslev, Nematollaah Shiri (2009)
An empirical comparison of ontology matching techniquesJournal of Information Science, 35
Mouhamadou Ba, G. Diallo (2012)
ServOMap and ServOMap-lt results for OAEI 2012
Ernesto Jiménez-Ruiz, B. Grau (2011)
LogMap: Logic-Based and Scalable Ontology Matching
P. Mitra, G. Wiederhold (2002)
Resolving Terminological Heterogeneity In Ontologies
P. Visser, Dean Jones, Trevor Bench-Capon, M. Shave (2007)
An Analysis of Ontology Mismatches; Heterogeneity versus Interoperability
Nora Maiz, M. Fahad, O. Boussaid, F. Bentayeb (2010)
Automatic Ontology Merging by Hierarchical Clustering and Inference Mechanisms
M. Fahad, N. Moalla, Abdelaziz Bouras (2011)
Towards ensuring Satisfiability of Merged Ontology
Konstantinos Kotis, G. Vouros, Konstantinos Stergiou (2006)
Towards automatic merging of domain ontologies: The HCONE-merge approachJ. Web Semant., 4
Mouhamadou Ba, G. Diallo (2013)
Large-scale biomedical ontology matching with ServOMapIrbm, 34
Jie Xie, Fei Liu, S. Guan (2011)
Tree-structure Based Ontology IntegrationJournal of Information Science, 37
(2009)
OWL 2 syntax document, OWL 2 Web ontology language structural specification and functional-style syntax
Alessandro Solimando, Ernesto Jiménez-Ruiz, G. Guerrini (2014)
Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings
Ernesto Jiménez-Ruiz, B. Grau, U. Sattler, Thomas Schneider, Rafael Llavori (2008)
Safe and Economic Re-Use of Ontologies: A Logic-Based Methodology and Tool Support
Salvatore Raunich, E. Rahm (2012)
Towards a Benchmark for Ontology Merging
J. Euzenat, P. Shvaiko (2007)
Ontology Matching
G. Miller (1995)
WordNet: A Lexical Database for EnglishCommun. ACM, 38
W. Djeddi, Tarek Khadir (2014)
A Novel Approach Using Context-Based Measure for Matching Large Scale Ontologies
Jaehong Kim, Minsu Jang, Youngguk Ha, J. Sohn, Sang-Jo Lee (2005)
MoA: OWL Ontology Merging and Alignment Tool for the Semantic Web
Ernesto Jiménez-Ruiz, B. Grau, Ian Horrocks (2012)
LogMap and LogMapLt results for OAEI 2012
N. Arch-int, Peraphon Sophatsathit (2003)
A Semantic Information Gathering Approach for Heterogeneous Information Sources on WWWJournal of Information Science, 29
A. Guzmán-Arenas, A. Cuevas-Rasgado (2010)
Knowledge accumulation through automatic merging of ontologiesExpert Syst. Appl., 37
P. Shvaiko, J. Euzenat (2013)
Ontology Matching: State of the Art and Future ChallengesIEEE Transactions on Knowledge and Data Engineering, 25
D Ngo, Z Bellahsene (2012)
YAM++ : a multi-strategy based approach for ontology matching task, knowledge engineering and knowledge managementLect Notes Comput Sci, 7603
M. Cheatham, P. Hitzler (2013)
String Similarity Metrics for Ontology Alignment
Salvatore Raunich, E. Rahm (2011)
ATOM: Automatic target-driven ontology merging2011 IEEE 27th International Conference on Data Engineering
R. Pottinger, P. Bernstein (2003)
Merging Models Based on Given Correspondences
DuyHoa Ngo, Zohra Bellahsene (2012)
YAM++ : A Multi-strategy Based Approach for Ontology Matching Task
Gerd Stumme, A. Maedche (2001)
FCA-MERGE: Bottom-Up Merging of Ontologies
DuyHoa Ngo, Zohra Bellahsene (2012)
YAM++ results for OAEI 2012
Ernesto Jiménez-Ruiz, B. Grau, Ian Horrocks, Rafael Llavori (2009)
Ontology Integration Using Mappings: Towards Getting the Right Logical Consequences
In the last decade, ontology matching and mapping research has shown a measurable progress. This topic draws substantial attention within the research community, though it is not fully researched so far and new complex and effective solutions are needed. Current works are limited in finding alignments or mappings between concepts of heterogeneous ontologies. But, once ontology mappings are found, then how they (or their class expressions) are to be integrated automatically is left open for the ontology merging research. This paper elaborates the mapping of class expressions of concepts and contributes an algorithm for their merging in an automatic ontology merging process without any human intervention. However, the challenge of mapping axiomatic definitions is the most difficult task for merging concept definitions of the source ontologies, but it reveals significant increase in precision and recall values. In addition, with the study of these algorithms, we conclude that ontology merging facilitates when one wants to get ontology with the better quality as the combined rich axioms are added in the merged ontology. We also discuss the results of our first successful participation in the Conference, OA4QA and Anatomy track of OAEI 2015.
Artificial Intelligence Review – Springer Journals
Published: Apr 25, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.