Building an effective and efficient background knowledge resource to enhance ontology matching
Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different model...
Ausführliche Beschreibung
Autor*in: |
Annane, Amina [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018transfer abstract |
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Umfang: |
18 |
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Übergeordnetes Werk: |
Enthalten in: Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group - 2012transfer abstract, science, services and agents on the World Wide Web, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:51 ; year:2018 ; pages:51-68 ; extent:18 |
Links: |
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DOI / URN: |
10.1016/j.websem.2018.04.001 |
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Katalog-ID: |
ELV044844417 |
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245 | 1 | 0 | |a Building an effective and efficient background knowledge resource to enhance ontology matching |
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520 | |a Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. | ||
520 | |a Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. | ||
650 | 7 | |a Background knowledge |2 Elsevier | |
650 | 7 | |a Supervised machine learning |2 Elsevier | |
650 | 7 | |a External resource |2 Elsevier | |
650 | 7 | |a Derivation |2 Elsevier | |
650 | 7 | |a Ontology matching |2 Elsevier | |
650 | 7 | |a Background knowledge selection |2 Elsevier | |
650 | 7 | |a Ontology alignment |2 Elsevier | |
650 | 7 | |a Indirect matching |2 Elsevier | |
650 | 7 | |a Anchoring |2 Elsevier | |
700 | 1 | |a Bellahsene, Zohra |4 oth | |
700 | 1 | |a Azouaou, Faiçal |4 oth | |
700 | 1 | |a Jonquet, Clement |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier |t Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group |d 2012transfer abstract |d science, services and agents on the World Wide Web |g Amsterdam [u.a.] |w (DE-627)ELV026326361 |
773 | 1 | 8 | |g volume:51 |g year:2018 |g pages:51-68 |g extent:18 |
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10.1016/j.websem.2018.04.001 doi GBV00000000000327_01.pica (DE-627)ELV044844417 (ELSEVIER)S1570-8268(18)30017-9 DE-627 ger DE-627 rakwb eng 660 VZ 540 VZ 610 VZ 44.90 bkl Annane, Amina verfasserin aut Building an effective and efficient background knowledge resource to enhance ontology matching 2018transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Background knowledge Elsevier Supervised machine learning Elsevier External resource Elsevier Derivation Elsevier Ontology matching Elsevier Background knowledge selection Elsevier Ontology alignment Elsevier Indirect matching Elsevier Anchoring Elsevier Bellahsene, Zohra oth Azouaou, Faiçal oth Jonquet, Clement oth Enthalten in Elsevier Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group 2012transfer abstract science, services and agents on the World Wide Web Amsterdam [u.a.] (DE-627)ELV026326361 volume:51 year:2018 pages:51-68 extent:18 https://doi.org/10.1016/j.websem.2018.04.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 51 2018 51-68 18 |
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10.1016/j.websem.2018.04.001 doi GBV00000000000327_01.pica (DE-627)ELV044844417 (ELSEVIER)S1570-8268(18)30017-9 DE-627 ger DE-627 rakwb eng 660 VZ 540 VZ 610 VZ 44.90 bkl Annane, Amina verfasserin aut Building an effective and efficient background knowledge resource to enhance ontology matching 2018transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Background knowledge Elsevier Supervised machine learning Elsevier External resource Elsevier Derivation Elsevier Ontology matching Elsevier Background knowledge selection Elsevier Ontology alignment Elsevier Indirect matching Elsevier Anchoring Elsevier Bellahsene, Zohra oth Azouaou, Faiçal oth Jonquet, Clement oth Enthalten in Elsevier Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group 2012transfer abstract science, services and agents on the World Wide Web Amsterdam [u.a.] (DE-627)ELV026326361 volume:51 year:2018 pages:51-68 extent:18 https://doi.org/10.1016/j.websem.2018.04.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 51 2018 51-68 18 |
allfields_unstemmed |
10.1016/j.websem.2018.04.001 doi GBV00000000000327_01.pica (DE-627)ELV044844417 (ELSEVIER)S1570-8268(18)30017-9 DE-627 ger DE-627 rakwb eng 660 VZ 540 VZ 610 VZ 44.90 bkl Annane, Amina verfasserin aut Building an effective and efficient background knowledge resource to enhance ontology matching 2018transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Background knowledge Elsevier Supervised machine learning Elsevier External resource Elsevier Derivation Elsevier Ontology matching Elsevier Background knowledge selection Elsevier Ontology alignment Elsevier Indirect matching Elsevier Anchoring Elsevier Bellahsene, Zohra oth Azouaou, Faiçal oth Jonquet, Clement oth Enthalten in Elsevier Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group 2012transfer abstract science, services and agents on the World Wide Web Amsterdam [u.a.] (DE-627)ELV026326361 volume:51 year:2018 pages:51-68 extent:18 https://doi.org/10.1016/j.websem.2018.04.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 51 2018 51-68 18 |
allfieldsGer |
10.1016/j.websem.2018.04.001 doi GBV00000000000327_01.pica (DE-627)ELV044844417 (ELSEVIER)S1570-8268(18)30017-9 DE-627 ger DE-627 rakwb eng 660 VZ 540 VZ 610 VZ 44.90 bkl Annane, Amina verfasserin aut Building an effective and efficient background knowledge resource to enhance ontology matching 2018transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Background knowledge Elsevier Supervised machine learning Elsevier External resource Elsevier Derivation Elsevier Ontology matching Elsevier Background knowledge selection Elsevier Ontology alignment Elsevier Indirect matching Elsevier Anchoring Elsevier Bellahsene, Zohra oth Azouaou, Faiçal oth Jonquet, Clement oth Enthalten in Elsevier Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group 2012transfer abstract science, services and agents on the World Wide Web Amsterdam [u.a.] (DE-627)ELV026326361 volume:51 year:2018 pages:51-68 extent:18 https://doi.org/10.1016/j.websem.2018.04.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 51 2018 51-68 18 |
allfieldsSound |
10.1016/j.websem.2018.04.001 doi GBV00000000000327_01.pica (DE-627)ELV044844417 (ELSEVIER)S1570-8268(18)30017-9 DE-627 ger DE-627 rakwb eng 660 VZ 540 VZ 610 VZ 44.90 bkl Annane, Amina verfasserin aut Building an effective and efficient background knowledge resource to enhance ontology matching 2018transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. Background knowledge Elsevier Supervised machine learning Elsevier External resource Elsevier Derivation Elsevier Ontology matching Elsevier Background knowledge selection Elsevier Ontology alignment Elsevier Indirect matching Elsevier Anchoring Elsevier Bellahsene, Zohra oth Azouaou, Faiçal oth Jonquet, Clement oth Enthalten in Elsevier Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group 2012transfer abstract science, services and agents on the World Wide Web Amsterdam [u.a.] (DE-627)ELV026326361 volume:51 year:2018 pages:51-68 extent:18 https://doi.org/10.1016/j.websem.2018.04.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 51 2018 51-68 18 |
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Enthalten in Catalytic decomposition of 4-phenoxyphenol to aromatics over palladium catalysts supported on activated carbon aerogel bearing sulfonic acid group Amsterdam [u.a.] volume:51 year:2018 pages:51-68 extent:18 |
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Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. |
abstractGer |
Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. |
abstract_unstemmed |
Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F -measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources. |
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