Details
| Original language | English |
|---|---|
| Title of host publication | The Semantic Web |
| Subtitle of host publication | 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28–June 1, 2023, Proceedings |
| Editors | Catia Pesquita, Daniel Faria, Ernesto Jimenez-Ruiz, Jamie McCusker, Mauro Dragoni, Anastasia Dimou, Raphael Troncy, Sven Hertling |
| Place of Publication | Cham |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 372-389 |
| Number of pages | 18 |
| ISBN (electronic) | 978-3-031-33455-9 |
| ISBN (print) | 9783031334542 |
| Publication status | Published - 2023 |
| Event | 20th International Conference on The Semantic Web, ESWC 2023 - Hersonissos, Greece Duration: 28 May 2023 → 1 Jun 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13870 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short). However, understanding trends and best practices in this rapidly growing field is hampered by a lack of standardized descriptions of these systems and an annotated corpus of such systems. To address these gaps, we leverage the results of a large-scale systematic mapping study collecting information about 470 SWeMLS papers and formalize these into one resource containing: (i) the SWeMLS ontology, (ii) the SWeMLS pattern library containing machine-actionable descriptions of 45 frequently occurring SWeMLS workflows, and (iii) SWEMLS-KG, a knowledge graph including machine-actionable metadata of the papers in terms of the SWeMLS ontology. This resource provides the first framework for semantically describing and organizing SWeMLS thus making a key impact in (1) understanding the status quo of the field based on the published paper corpus and (2) enticing the uptake of machine-processable system documentation in the SWeMLS area.
Keywords
- Knowledge Graphs, Machine Learning, Neuro-symbolic System, Semantic Web
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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The Semantic Web : 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28–June 1, 2023, Proceedings. ed. / Catia Pesquita; Daniel Faria; Ernesto Jimenez-Ruiz; Jamie McCusker; Mauro Dragoni; Anastasia Dimou; Raphael Troncy; Sven Hertling. Cham: Springer Science and Business Media Deutschland GmbH, 2023. p. 372-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13870 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
AU - Ekaputra, Fajar J.
AU - Llugiqi, Majlinda
AU - Sabou, Marta
AU - Ekelhart, Andreas
AU - Paulheim, Heiko
AU - Breit, Anna
AU - Revenko, Artem
AU - Waltersdorfer, Laura
AU - Farfar, Kheir Eddine
AU - Auer, Sören
N1 - Funding Information: This work has been supported by the Austrian Science Fund (FWF) under grant V0745 (HOnEst) and FFG Project OBARIS (Grant Agreement No 877389). SBA Research (SBA-K1) is a COMET Center within the COMET-Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW, and the federal state of Vienna. The COMET Programme is managed by FFG. Moreover, financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, DFG NFDI4DataScience (No. 460234259) and ERC ScienceGRAPH (GA ID: 819536) is gratefully acknowledged.
PY - 2023
Y1 - 2023
N2 - The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short). However, understanding trends and best practices in this rapidly growing field is hampered by a lack of standardized descriptions of these systems and an annotated corpus of such systems. To address these gaps, we leverage the results of a large-scale systematic mapping study collecting information about 470 SWeMLS papers and formalize these into one resource containing: (i) the SWeMLS ontology, (ii) the SWeMLS pattern library containing machine-actionable descriptions of 45 frequently occurring SWeMLS workflows, and (iii) SWEMLS-KG, a knowledge graph including machine-actionable metadata of the papers in terms of the SWeMLS ontology. This resource provides the first framework for semantically describing and organizing SWeMLS thus making a key impact in (1) understanding the status quo of the field based on the published paper corpus and (2) enticing the uptake of machine-processable system documentation in the SWeMLS area.
AB - The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short). However, understanding trends and best practices in this rapidly growing field is hampered by a lack of standardized descriptions of these systems and an annotated corpus of such systems. To address these gaps, we leverage the results of a large-scale systematic mapping study collecting information about 470 SWeMLS papers and formalize these into one resource containing: (i) the SWeMLS ontology, (ii) the SWeMLS pattern library containing machine-actionable descriptions of 45 frequently occurring SWeMLS workflows, and (iii) SWEMLS-KG, a knowledge graph including machine-actionable metadata of the papers in terms of the SWeMLS ontology. This resource provides the first framework for semantically describing and organizing SWeMLS thus making a key impact in (1) understanding the status quo of the field based on the published paper corpus and (2) enticing the uptake of machine-processable system documentation in the SWeMLS area.
KW - Knowledge Graphs
KW - Machine Learning
KW - Neuro-symbolic System
KW - Semantic Web
UR - http://www.scopus.com/inward/record.url?scp=85163321801&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2303.15113
DO - 10.48550/arXiv.2303.15113
M3 - Conference contribution
AN - SCOPUS:85163321801
SN - 9783031334542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 372
EP - 389
BT - The Semantic Web
A2 - Pesquita, Catia
A2 - Faria, Daniel
A2 - Jimenez-Ruiz, Ernesto
A2 - McCusker, Jamie
A2 - Dragoni, Mauro
A2 - Dimou, Anastasia
A2 - Troncy, Raphael
A2 - Hertling, Sven
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 20th International Conference on The Semantic Web, ESWC 2023
Y2 - 28 May 2023 through 1 June 2023
ER -