Details
| Original language | English |
|---|---|
| Title of host publication | The Semantic Web – ISWC 2020 |
| Subtitle of host publication | 19th International Semantic Web Conference, 2020, Proceedings |
| Editors | Jeff Z. Pan, Valentina Tamma, Claudia d’Amato, Krzysztof Janowicz, Bo Fu, Axel Polleres, Oshani Seneviratne, Lalana Kagal |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 276-293 |
| Number of pages | 18 |
| ISBN (print) | 9783030624187 |
| Publication status | Published - 1 Nov 2020 |
| Externally published | Yes |
| Event | 19th International Semantic Web Conference, ISWC 2020 - Athens, Greece Duration: 2 Nov 2020 → 6 Nov 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12506 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
Keywords
- Functions, Knowledge graph creation, Mapping rules
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, 2020, Proceedings. ed. / Jeff Z. Pan; Valentina Tamma; Claudia d’Amato; Krzysztof Janowicz; Bo Fu; Axel Polleres; Oshani Seneviratne; Lalana Kagal. Springer Science and Business Media Deutschland GmbH, 2020. p. 276-293 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12506 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - FunMap
T2 - 19th International Semantic Web Conference, ISWC 2020
AU - Jozashoori, Samaneh
AU - Chaves-Fraga, David
AU - Iglesias, Enrique
AU - Vidal, Maria Esther
AU - Corcho, Oscar
N1 - Funding information: Acknowledgments. This work has been partially supported by the EU H2020 project iASiS No. 727658, the ERAMed project P4-LUCAT No. 53000015, Ministerio de Economía, Industria y Competitividad, and EU FEDER funds under the DATOS 4.0: RETOS Y SOLUCIONES - UPM Spanish national project (TIN2016-78011-C4-4-R) and by the FPI grant (BES-2017-082511).
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
AB - Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
KW - Functions
KW - Knowledge graph creation
KW - Mapping rules
UR - http://www.scopus.com/inward/record.url?scp=85096599117&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2008.13482
DO - 10.48550/arXiv.2008.13482
M3 - Conference contribution
AN - SCOPUS:85096599117
SN - 9783030624187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 276
EP - 293
BT - The Semantic Web – ISWC 2020
A2 - Pan, Jeff Z.
A2 - Tamma, Valentina
A2 - d’Amato, Claudia
A2 - Janowicz, Krzysztof
A2 - Fu, Bo
A2 - Polleres, Axel
A2 - Seneviratne, Oshani
A2 - Kagal, Lalana
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 November 2020 through 6 November 2020
ER -