Details
| Original language | English |
|---|---|
| Title of host publication | Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Proceedings |
| Editors | Sven Hartmann, Josef Küng, Gabriele Kotsis, Ismail Khalil, A Min Tjoa |
| Place of Publication | Cham |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 179-194 |
| Number of pages | 16 |
| ISBN (electronic) | 978-3-030-59051-2 |
| ISBN (print) | 9783030590505 |
| Publication status | Published - 8 Feb 2020 |
| Event | 31st International Conference on Database and Expert Systems Applications, DEXA 2020 - Bratislava, Slovakia Duration: 14 Sept 2020 → 17 Sept 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12392 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Industry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans$$^*$$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Proceedings. ed. / Sven Hartmann; Josef Küng; Gabriele Kotsis; Ismail Khalil; A Min Tjoa. Cham: Springer Science and Business Media Deutschland GmbH, 2020. p. 179-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12392 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Unveiling relations in the industry 4.0 standards landscape based on knowledge graph embeddings
AU - Rivas, Ariam
AU - Grangel-González, Irlán
AU - Collarana, Diego
AU - Lehmann, Jens
AU - Vidal, Maria Esther
N1 - Funding Information: Acknowledgments. Ariam Rivas is supported by the German Academic Exchange Service (DAAD). This work has been partially funded by the EU H2020 Projects IASIS (GA 727658) and LAMBDA (GA 809965).
PY - 2020/2/8
Y1 - 2020/2/8
N2 - Industry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans$$^*$$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
AB - Industry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans$$^*$$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
UR - http://www.scopus.com/inward/record.url?scp=85091602176&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59051-2_12
DO - 10.1007/978-3-030-59051-2_12
M3 - Conference contribution
AN - SCOPUS:85091602176
SN - 9783030590505
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 179
EP - 194
BT - Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Proceedings
A2 - Hartmann, Sven
A2 - Küng, Josef
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 31st International Conference on Database and Expert Systems Applications, DEXA 2020
Y2 - 14 September 2020 through 17 September 2020
ER -