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Title: Causal Relationship over Knowledge Graphs
Authors: Huang, Hao
Editors: Al Hasan, MohammadXiong, Li
Publishers version:
Issue Date: 2022
Book: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
Page Start: 5116
Page End: 5119
Abstract: Causality has been discussed for centuries, and the theory of causal inference over tabular data has been broadly studied and utilized in multiple disciplines. However, only a few works attempt to infer the causality while exploiting the meaning of the data represented in a data structure like knowledge graph. These works offer a glance at the possibilities of causal inference over knowledge graphs, but do not yet consider the metadata, e.g., cardinalities, class subsumption and overlap, and integrity constraints. We propose CareKG, a new formalism to express causal relationships among concepts, i.e., classes and relations, and enable causal queries over knowledge graphs using semantics of metadata. We empirically evaluate the expressiveness of CareKG in a synthetic knowledge graph concerning cardinalities, class subsumption and overlap, integrity constraints. Our initial results indicate that CareKG can represent and measure causal relations with some semantics which are uncovered by state-of-the-art approaches.
Keywords: causal inference; knowledge graphs; semantic data models; Konferenzschrift
Type: bookPart; Text
Publishing status: publishedVersion
DDC: 004
License: CC BY-NC-SA 4.0 Unported
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Appears in Collections:Informatik

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Huang, Hao, 2022. Causal Relationship over Knowledge Graphs. In: (Hrsg.)Mohammad Al Hasan and Li Xiong. ISBN 978-1-4503-9236-5
Huang, H. (2022) “Causal Relationship over Knowledge Graphs.” doi:
Huang H. Causal Relationship over Knowledge Graphs. In: , editorAl Hasan M, Xiong L. 2022.
Huang, H. (2022). Causal Relationship over Knowledge Graphs.
Huang H. Causal Relationship over Knowledge Graphs. In: , ed.Al Hasan M, Xiong L ; 2022. doi:

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