The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
PARIS: probabilistic alignment of relations, instances, and schema
Abstract
One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present paris, an approach for the automatic alignment of ontologies. paris aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows paris to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.
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Cites
Yago: a core of semantic knowledge
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242667 · Cited by 2,512
Self-citationDBpedia: A Nucleus for a Web of Open Data
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Towards a standard upper ontology
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Web-scale information extraction in knowitall: (preliminary results)
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Schema and ontology matching with COMA++
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Deriving a large scale taxonomy from Wikipedia
Ponzetto, S. P., & Strube, M. (2007) · MADOC (University of Mannheim) · Cited by 443
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Self-citationBig data challenge: a data management perspective
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-3903-7 · Cited by 339
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing
Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, M., Papotti, P., Tang, N., & Ye, Y. (2015) · Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2723372.2749431 · Cited by 279
A benchmarking study of embedding-based entity alignment for knowledge graphs
Sun, Z., Zhang, Q., Hu, W., Wang, C., Chen, M., Akrami, F., & Li, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407828 · Cited by 265
MMKG: Multi-modal Knowledge Graphs
Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., & Rosenblum, D. S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-21348-0_30 · Cited by 257
Metadata
- Authors
- Suchanek, F. M., Abiteboul, S., & Senellart, P.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2011-11
- Volume
- 5
- Issue
- 3
- Pages
- 157-168
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W1597082186
- Cited by
- 358
- References
- 16
- FWCI
- 14.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- Suchanek, F. M., Abiteboul, S., & Senellart, P. (2011). PARIS: probabilistic alignment of relations, instances, and schema. Proceedings of the VLDB Endowment. https://doi.org/10.14778/2078331.2078332
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