Abstract
| Original language | English |
|---|---|
| Title of host publication | K-CAP '19 Proceedings of the 10th International Conference on Knowledge Capture |
| Publisher | ACM |
| Pages | 77-84 |
| Number of pages | 8 |
| ISBN (Print) | 9781450370080 |
| DOIs | |
| Publication status | Published - 19 Nov 2019 |
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On the Impact of sameAs on Schema Matching. / Raad, Joe; Acar, Erman; Schlobach, Stefan.
K-CAP '19 Proceedings of the 10th International Conference on Knowledge Capture. ACM, 2019. p. 77-84.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - On the Impact of sameAs on Schema Matching
AU - Raad, Joe
AU - Acar, Erman
AU - Schlobach, Stefan
PY - 2019/11/19
Y1 - 2019/11/19
N2 - In a large and decentralised knowledge representation system such as the Web of Data, it is common for data sets to overlap. In the absence of a central naming authority, semantic heterogeneity is inevitable as such overlapping contents are described using different schemas. To overcome this problem, a number of solutions have automated the integration of these data sets by matching their schemas. In this work we focus on a specific category of these solutions, which relies on the concepts’ extension for matching the schemas (i.e., instance-based methods). Rather than introducing a new approach for the task of schema matching, this work studies the effect of exploiting the semantics of owl:sameAs in such instance-based methods. For this empirical analysis, we investigate more than 900K concepts extracted from the Web, and make use of over 35B implicit identity assertions to study their impact. The experiments show that despite the growing doubts over their quality, exploiting owl:sameAs assertions extracted from the Web can improve instance-based schema matching techniques.
AB - In a large and decentralised knowledge representation system such as the Web of Data, it is common for data sets to overlap. In the absence of a central naming authority, semantic heterogeneity is inevitable as such overlapping contents are described using different schemas. To overcome this problem, a number of solutions have automated the integration of these data sets by matching their schemas. In this work we focus on a specific category of these solutions, which relies on the concepts’ extension for matching the schemas (i.e., instance-based methods). Rather than introducing a new approach for the task of schema matching, this work studies the effect of exploiting the semantics of owl:sameAs in such instance-based methods. For this empirical analysis, we investigate more than 900K concepts extracted from the Web, and make use of over 35B implicit identity assertions to study their impact. The experiments show that despite the growing doubts over their quality, exploiting owl:sameAs assertions extracted from the Web can improve instance-based schema matching techniques.
U2 - 10.1145/3360901.3364442
DO - 10.1145/3360901.3364442
M3 - Conference contribution
SN - 9781450370080
SP - 77
EP - 84
BT - K-CAP '19 Proceedings of the 10th International Conference on Knowledge Capture
PB - ACM
ER -