<div class="csl-bib-body">
<div class="csl-entry">Schrott, J., Jakubowski, M., & Hose, K. (2026). <i>A Graph-Native Approach to Normalization</i>. arXiv. https://doi.org/10.48550/arXiv.2603.02995</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/227053
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dc.description.abstract
In recent years, knowledge graphs (KGs) - in particular in the form of labeled property graphs (LPGs) - have become essential components in a broad range of applications. Although the absence of strict schemas for KGs facilitates structural issues that lead to redundancies and subsequently to inconsistencies and anomalies, the problem of KG quality has so far received only little attention. Inspired by normalization using functional dependencies for relational data, a first approach exploiting dependencies within nodes has been proposed. However, real-world KGs also expose functional dependencies involving edges. In this paper, we therefore propose graph-native normalization, which considers dependencies within nodes, edges, and their combination. We define a range of graph-native normal forms and graph object functional dependencies and propose algorithms for transforming graphs accordingly. We evaluate our contributions using a broad range of synthetic and native graph datasets.
en
dc.language.iso
en
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dc.subject
Knowledge Graphs
en
dc.subject
Labeled Property Graphs
en
dc.subject
Knowledge Graph Quality
en
dc.subject
Normalization
en
dc.subject
Redundancy
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dc.title
A Graph-Native Approach to Normalization
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.identifier.arxiv
2603.02995
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tuw.researchTopic.id
I1
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tuw.researchTopic.id
C4
-
tuw.researchTopic.id
I4
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.name
Mathematical and Algorithmic Foundations
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
15
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tuw.researchTopic.value
15
-
tuw.researchTopic.value
70
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tuw.linking
https://github.com/dmki-tuwien/lpg-normalization
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tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publication.orgunit
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML)
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tuw.publisher.doi
10.48550/arXiv.2603.02995
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dc.description.numberOfPages
14
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tuw.author.orcid
0000-0003-2689-0876
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tuw.author.orcid
0000-0002-7420-1337
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tuw.author.orcid
0000-0001-7025-8099
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tuw.publisher.server
arXiv
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
80
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wb.sciencebranch.value
20
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item.fulltext
no Fulltext
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item.grantfulltext
none
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item.openairecristype
http://purl.org/coar/resource_type/c_816b
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item.cerifentitytype
Publications
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item.languageiso639-1
en
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item.openairetype
preprint
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crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence