Towards a Standardized Description of Semantic Web Machine Learning Systems

Ekaputra, Fajar J.; Waltersdorfer, Laura; Breit, Anna; Sabou, Marta

Record link:

http://hdl.handle.net/20.500.12708/190042

Title:

Towards a Standardized Description of Semantic Web Machine Learning Systems

Citation:

Ekaputra, F. J., Waltersdorfer, L., Breit, A., & Sabou, M. (2022). Towards a Standardized Description of Semantic Web Machine Learning Systems. In U. Simsek, D. Chaves-Fraga, T. Pellegrini, & S. Vahdat (Eds.), Proceedings of Poster and Demo Track and Workshop Track of the 18th International Conference on Semantic Systems, co-located with 18th International Conference on Semantic Systems (SEMANTiCS 2022).

Publication Type:

Inproceedings - Poster Contribution

Language:

English

Authors:

Ekaputra, Fajar J.
Waltersdorfer, Laura
Breit, Anna
Sabou, Marta

Organisational Unit:

E194-04 - Forschungsbereich Data Science

Published in:

Proceedings of Poster and Demo Track and Workshop Track of the 18th International Conference on Semantic Systems, co-located with 18th International Conference on Semantic Systems (SEMANTiCS 2022)

Volume:

3235

Date (published):

2022

Event name:

Posters, Demos and Workshops at SEMANTiCS 2022

Event date:

13-Sep-2022 - 15-Sep-2022

Event place:

Vienna, Austria

Number of Pages:

Peer reviewed:

Yes

Keywords:

machine learning; neuro-symbolic systems; semantic web

Abstract:

In this paper, we report on our proposed approach towards a standardized description for systems combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community (SWeMLS), which is one of lessons learned from our large-scale survey (476 papers) on the topic. We elaborate the key information that should be described of SWeMLS and selected methods to support its documentation.

Project title:

Ontology-Based ARtificial Intelligence in the Environmental Sector: 877389 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)

Link (external):

https://ceur-ws.org/Vol-3235/

Research Areas:

Information Systems Engineering: 100%

Science Branch:

1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%

Appears in Collections:

Conference Paper

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