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A performant and incremental algorithm for knowledge graph entity typing

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Abstract

Knowledge Graph Entity Typing (KGET) is a subtask of knowledge graph completion, which aims at inferring missing entity types by utilizing existing type knowledge and triple knowledge of the knowledge graph. Previous knowledge graph embedding (KGE) algorithms infer entity types through trained entity embeddings. However, for new unseen entities, KGE models encounter obstacles in inferring their types. In addition, it is also difficult for KGE models to improve the performance incrementally with the increase of added data. In this paper, we propose a statistic-based KGET algorithm which aims to take both performance and incrementality into consideration. The algorithm aggregates the neighborhood information and type co-occurrence information of target entities to infer their types. Specifically, we first compute the type probability distribution of the target entity in the semantic context of given fact triple. Then the probability information of fact triples involved in the target entity is aggregated. In addition to local neighborhood information, we also consider capturing global type co-occurrence information for target entities to enhance inference performance. Extensive experiments show that our algorithm outperforms previous statistics-based KGET algorithms and even some KGE models. Finally, we design an incremental inference experiment, which verifies the superiority of our algorithm in predicting the types of new entities, and the experiment also verifies that our algorithm has excellent incremental property.

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  1. http://www.openkg.cn/dataset/7lore

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Acknowledgements

Thanks for the computing resources provided by the Supercomputing Center of Lanzhou University.

Funding

This work is supported by the National Key Research and Development Program of China (Grant No. 2021YFF1201203), the National Natural Science Foundation of China (Grant No. 62227807), the Natural Science Foundation of Gansu Province (Grant No. 20JR10RA605), and the Fundamental Research Funds for the Central Universities (lzujbky-2021-66).

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Authors and Affiliations

  1. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Zepeng Li, Rikui Huang, Minyu Zhai, Zhenwen Zhang & Bin Hu

  2. School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, Beijing, China

    Bin Hu

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200000, Shanghai, China

    Bin Hu

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  1. Zepeng Li
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  2. Rikui Huang
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  3. Minyu Zhai
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  4. Zhenwen Zhang
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  5. Bin Hu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zepeng Li, Rikui Huang, Zhenwen Zhang and Bin Hu; The first draft of the manuscript was written by Zepeng Li, Rikui Huang and Minyu Zhai and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Bin Hu.

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Li, Z., Huang, R., Zhai, M. et al. A performant and incremental algorithm for knowledge graph entity typing. World Wide Web 26, 2453–2470 (2023). https://doi.org/10.1007/s11280-023-01155-1

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