Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching
Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challeng...
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| Published in | International journal on semantic web and information systems Vol. 18; no. 1; pp. 1 - 18 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hershey
IGI Global
01.01.2022
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| Online Access | Get full text |
| ISSN | 1552-6283 1552-6291 |
| DOI | 10.4018/IJSWIS.297042 |
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| Abstract | Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task. |
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| AbstractList | Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task. Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its time and large memory space consumption. The actual solution to this problem is ontology partitioning, which is also challenging. This paper presents DeepOM, an ontology matching system, to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists of creating semantic embeddings for concepts of input ontologies using a reference ontology and uses them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies and its comparison with different ontology matching systems which have participated to the same test challenge are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task. |
| Audience | Academic |
| Author | Bouarfa, Hafida Khoudja, Meriem Ali Fareh, Messaouda |
| AuthorAffiliation | LRDSI Laboratory, Faculty of Science, University of Blida 1, Algeria |
| AuthorAffiliation_xml | – name: LRDSI Laboratory, Faculty of Science, University of Blida 1, Algeria |
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| CitedBy_id | crossref_primary_10_1038_s41598_023_49869_6 crossref_primary_10_1007_s00500_024_09938_y crossref_primary_10_1016_j_knosys_2024_112392 crossref_primary_10_4018_IJSWIS_334556 crossref_primary_10_1109_ACCESS_2024_3393909 crossref_primary_10_1007_s11220_023_00457_y crossref_primary_10_1016_j_techfore_2024_123395 crossref_primary_10_3934_era_2023291 crossref_primary_10_31083_j_fbl2902075 crossref_primary_10_1016_j_rcim_2024_102837 |
| Cites_doi | 10.4018/jswis.2007040101 10.1007/978-3-642-32518-2_17 10.1007/978-3-319-12277-9_4 10.1007/s12652-018-0919-8 10.1109/TKDE.2011.253 10.1145/3412841.3442059 10.1038/s41597-019-0055-0 10.4018/IJSWIS.2015070102 10.18653/v1/D15-1289 10.1109/ICASS.2018.8652049 10.3928/1081597X-20190124-02 10.1145/3365109.3368779 10.1007/s00521-020-05410-8 10.1109/ICHI-W.2018.00012 10.4018/IJSWIS.2020010103 10.1016/j.eswa.2014.08.032 10.4018/IJSSCI.2020070101 10.3233/SW-190366 10.1145/3297280.3297507 10.1016/j.jocs.2017.11.006 10.1145/2350716.2350757 10.1109/ICWR.2016.7498461 10.1007/s10586-017-0844-1 10.1504/IJBET.2019.102120 |
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| References | S.Hertling (IJSWIS.297042-13) 2020; 2788 M.Ali Khoudja (IJSWIS.297042-1) 2018 E.Jiménez-Ruiz (IJSWIS.297042-14) 2018; 2288 IJSWIS.297042-9 IJSWIS.297042-7 IJSWIS.297042-6 IJSWIS.297042-5 IJSWIS.297042-4 IJSWIS.297042-3 IJSWIS.297042-2 IJSWIS.297042-0 A.Laadhar (IJSWIS.297042-16) 2018 IJSWIS.297042-23 IJSWIS.297042-22 IJSWIS.297042-25 IJSWIS.297042-24 IJSWIS.297042-27 IJSWIS.297042-26 IJSWIS.297042-29 IJSWIS.297042-28 IJSWIS.297042-30 IJSWIS.297042-10 I.Nkisi-Orji (IJSWIS.297042-21) 2018 IJSWIS.297042-32 IJSWIS.297042-31 IJSWIS.297042-19 M.Monych (IJSWIS.297042-20) 2020; 2788 M. T.Dhouib (IJSWIS.297042-8) 2019 IJSWIS.297042-12 IJSWIS.297042-34 IJSWIS.297042-11 IJSWIS.297042-33 IJSWIS.297042-15 IJSWIS.297042-18 IJSWIS.297042-17 |
| References_xml | – ident: IJSWIS.297042-32 doi: 10.4018/jswis.2007040101 – ident: IJSWIS.297042-23 – ident: IJSWIS.297042-25 – ident: IJSWIS.297042-19 – ident: IJSWIS.297042-9 doi: 10.1007/978-3-642-32518-2_17 – ident: IJSWIS.297042-34 doi: 10.1007/978-3-319-12277-9_4 – volume: 2788 start-page: 181 year: 2020 ident: IJSWIS.297042-20 article-title: DESKMatcher. publication-title: CEUR Workshop Proceedings – ident: IJSWIS.297042-11 doi: 10.1007/s12652-018-0919-8 – ident: IJSWIS.297042-27 doi: 10.1109/TKDE.2011.253 – ident: IJSWIS.297042-5 doi: 10.1145/3412841.3442059 – ident: IJSWIS.297042-33 doi: 10.1038/s41597-019-0055-0 – ident: IJSWIS.297042-15 doi: 10.4018/IJSWIS.2015070102 – start-page: 557 year: 2018 ident: IJSWIS.297042-21 article-title: Ontology alignment based on word embedding and random forest classification. publication-title: Joint European Conference on Machine Learning and Knowledge Discovery in Databases – ident: IJSWIS.297042-30 doi: 10.18653/v1/D15-1289 – ident: IJSWIS.297042-2 doi: 10.1109/ICASS.2018.8652049 – volume: 2288 start-page: 13 year: 2018 ident: IJSWIS.297042-14 article-title: We divide, you conquer: From large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings. publication-title: CEUR Workshop Proceedings – ident: IJSWIS.297042-7 doi: 10.3928/1081597X-20190124-02 – ident: IJSWIS.297042-18 doi: 10.1145/3365109.3368779 – ident: IJSWIS.297042-26 doi: 10.1007/s00521-020-05410-8 – ident: IJSWIS.297042-6 doi: 10.1109/ICHI-W.2018.00012 – start-page: 542 year: 2018 ident: IJSWIS.297042-1 article-title: A new supervised learning based ontology matching approach using neural networks. publication-title: International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning – ident: IJSWIS.297042-31 doi: 10.4018/IJSWIS.2020010103 – volume: 2788 start-page: 168 year: 2020 ident: IJSWIS.297042-13 article-title: ATBox results for OAEI 2020. publication-title: CEUR Workshop Proceedings – ident: IJSWIS.297042-22 doi: 10.1016/j.eswa.2014.08.032 – ident: IJSWIS.297042-12 – ident: IJSWIS.297042-10 doi: 10.4018/IJSSCI.2020070101 – ident: IJSWIS.297042-28 doi: 10.3233/SW-190366 – ident: IJSWIS.297042-17 doi: 10.1145/3297280.3297507 – ident: IJSWIS.297042-0 doi: 10.1016/j.jocs.2017.11.006 – start-page: 191 year: 2019 ident: IJSWIS.297042-8 article-title: An ontology alignment approach combining word embedding and the radius measure. publication-title: International Conference on Semantic Systems – start-page: 220 year: 2018 ident: IJSWIS.297042-16 article-title: Partitioning and matching tuning of large biomedical ontologies. publication-title: 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference (OM 2018) – ident: IJSWIS.297042-29 doi: 10.1145/2350716.2350757 – ident: IJSWIS.297042-3 doi: 10.1109/ICWR.2016.7498461 – ident: IJSWIS.297042-24 doi: 10.1007/s10586-017-0844-1 – ident: IJSWIS.297042-4 doi: 10.1504/IJBET.2019.102120 |
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| SubjectTerms | Analysis Coders Computational linguistics Deep learning Heterogeneity Language processing Matching Natural language interfaces Partitioning |
| Title | Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching |
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