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Grier: graph repairing based on iterative embedding and rules

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Abstract

Identifying and repairing the erroneous and missing information in the graphs is crucial. Rule-based studies rely on graph quality rules to detect the inconsistencies among different entities and make a one-time repair to resolve these inconsistencies. The missing data can be predicted and imputed by graph embedding methods by preserving the inherent structure of a reliable graph. However, both lines of way need more evidence to improve. In this paper, we propose Grier, a novel repairing method to enrich the evidence by taking advantage of rule-based repairing and graph embedding. Specifically, Grier iteratively learns the graph embeddings with guidance from logical rules, which has significant power in knowledge acquisition and inference. The erroneous triples are detected and repaired by evaluating the correctness of the triples in the embedding space. The newly repaired triples are used as training data to update the embedding module for better learning. Extensive experiments on three graphs demonstrate the effectiveness of Grier even with very few rules.

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Notes

  1. In this paper, we restrict the logical rules to be Horn clause rules, where the premise of each rule contains a set of clauses and the conclusion of each rule contains only one clause.

  2. AMIE3 provides two types of confidence, i.e., standard confidence and PCA confidence. In this paper, we use PCA confidence.

  3. https://github.com/lajus/amie.

  4. https://github.com/TimDettmers/ConvE.

  5. https://github.com/iieir-km/RUGE.

  6. https://github.com/iieir-km/ComplEx-NNE_AER.

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Acknowledgements

This paper was partially supported by Natural Science Foundation of Zhejiang Province (No. LQ22F020032), National Natural Science Foundation of China (No. 62202132), and National Key Research and Development Program of China (No. 2022YFE0199300)

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Chen Ye, Hong Xu, Hua Zhang, Yifan Wu & Guojun Dai

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  1. Chen Ye
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  2. Hong Xu
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  3. Hua Zhang
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  4. Yifan Wu
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  5. Guojun Dai
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Contributions

CY involved in methodology, software, validation, formal analysis, writing—original draft and visualization; HX involved in software, data curation and visualization; HZ and YW involved in conceptualization, resources, writing—review and editing, supervision, project administration and funding acquisition; GD involved in resources and funding acquisition.

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Correspondence to Hua Zhang.

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Ye, C., Xu, H., Zhang, H. et al. Grier: graph repairing based on iterative embedding and rules. Knowl Inf Syst 65, 3273–3294 (2023). https://doi.org/10.1007/s10115-023-01866-x

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