Research output per year
Research output per year
Project Details
Financing body
FFG
Description
The FFG-funded OBARIS project (Ontology-Based Artificial Intelligence in Environmental Sector) aims to advance the status quo in the area of auditable semantic artificial intelligence systems (SAISs), by investigating both conceptual aspects of these systems as well as a technology stack that facilitates transposing these system types into concrete settings.
Key findings
The project goals materialized in a number of concrete, innovative results including:
(i) a taxonomy of semantic AI systems that will foster the understanding of such system typologies by the research community and industry adopters alike;
(ii) a semantic AI framework offering a technology stack for creating hybrid semantic – machine learning data processing pipelines;
(iii) guidelines, frameworks and a research prototype that enable building and monitoring auditable, transparent and trustworthy SAISs; and
(iv) demonstrators that apply the developed methods and components within two use cases in the environmental domain.
(i) a taxonomy of semantic AI systems that will foster the understanding of such system typologies by the research community and industry adopters alike;
(ii) a semantic AI framework offering a technology stack for creating hybrid semantic – machine learning data processing pipelines;
(iii) guidelines, frameworks and a research prototype that enable building and monitoring auditable, transparent and trustworthy SAISs; and
(iv) demonstrators that apply the developed methods and components within two use cases in the environmental domain.
| Acronym | OBARIS |
|---|---|
| Status | Finished |
| Effective start/end date | 1/02/20 → 31/10/22 |
Collaborative partners
- Vienna University of Economics and Business
- Institute of Information Systems Engineering (lead)
- Semantic Web Company GmbH (Project partner)
- Umweltbundesamt GmbH (Project partner)
- HPC Consulting (Project partner)
Research output
-
Combining Machine Learning and Semantic Web: A Systematic Mapping Study
Breit, A., Waltersdorfer, L., Ekaputra, F. J., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., ten Teije, A. & Van Harmelen, F., 17 Jul 2023, In: ACM Computing Surveys. 55, 14s, 313.Publication: Scientific journal › Journal article › peer-review
Open Access -
Combining Semantic Web and Machine Learning for Auditable Legal Key Element Extraction
Breit, A., Waltersdorfer, L., Ekaputra, F. J., Karampatakis, S., Miksa, T. & Käfer, G., Jun 2023, The Semantic Web: 20th International Conference, ESWC 2023. Cham: Springer, p. 609-624 (Lecture Notes in Computer Science; No. 13870).Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings
Open Access -
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., Revenko, A., Waltersdorfer, L., Farfar, K. E. & Auer, S., Jun 2023, The Semantic Web: 20th International Conference, ESWC 2023. Cham: Springer, p. 372–389 (Lecture Notes in Computer Science; No. 13870).Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings
Open Access