Feature Engineering For Knowledge Graphs
Feature Engineering for Knowledge Graphs is a methodological framework within domain-specific feature design that systematically constructs and transforms structural, semantic, and relational attributes from knowledge graph data to enhance the performance of machine learning models in knowledge-driven tasks.
20
Publications
768
Citations
79
Authors
40
Institutions
Publications per year
2017–2026
13
20
Neuro-symbolic representation learning on biological knowledge graphs
Mona Alshahrani, Omar Maddouri, Akira R. Kinjo et al. · Bioinformatics · 2017 · 151 citations · Full text
Brainwash: A data system for feature engineering
Michael R. Anderson, Dolan Antenucci, Victor Bittorf et al. · 2013 · 103 citations
A knowledge graph empowered online learning framework for access control decision-making
Mingshan You, Jiao Yin, Hua Wang et al. · World Wide Web · 2022 · 78 citations · Full text
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Battelle
Columbus, United States
1 h-index
3 publications
48 citations
AT&T (United States)
Bedminster, United States
1 h-index
1 publications
15 citations
Robert Bosch (Taiwan)
Taipei, Taiwan
1 h-index
3 publications
72 citations
University of Oxford
Oxford, United Kingdom
1 h-index
1 publications
19 citations
KU Leuven
Leuven, Belgium
1 h-index
2 publications
20 citations
Arizona State University
Tempe, United States
1 h-index
3 publications
48 citations
University of Information Technology and Management in Rzeszow
Rzeszów, Poland
1 h-index
2 publications
26 citations
Zhejiang Lab
Hangzhou, China
1 h-index
3 publications
57 citations
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1 publications · 23 citations
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1 publications · 29 citations
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1 publications · 39 citations
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Conference · 1 publications · 28 citations
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1 publications · 151 citations
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