Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions Article Swipe
Yangshengyan Liu
,
Fu Gu
,
Xinjian Gu
,
Yijie Wu
,
Jianfeng Guo
,
Jin Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s44196-022-00097-2
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s44196-022-00097-2
Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s44196-022-00097-2
- https://link.springer.com/content/pdf/10.1007/s44196-022-00097-2.pdf
- OA Status
- gold
- Cited By
- 17
- References
- 85
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283786071
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283786071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s44196-022-00097-2Digital Object Identifier
- Title
-
Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource ConditionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-03Full publication date if available
- Authors
-
Yangshengyan Liu, Fu Gu, Xinjian Gu, Yijie Wu, Jianfeng Guo, Jin ZhangList of authors in order
- Landing page
-
https://doi.org/10.1007/s44196-022-00097-2Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s44196-022-00097-2.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s44196-022-00097-2.pdfDirect OA link when available
- Concepts
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Computer science, Schema (genetic algorithms), Graph, Knowledge graph, Resource (disambiguation), Knowledge management, Artificial intelligence, Machine learning, Theoretical computer science, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 5, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
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85Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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