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Open knowledge base canonicalization with multi-task learning

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Abstract

The construction of large open knowledge bases (OKBs) is integral to many knowledge-driven applications on the world wide web such as web search. However, noun phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. Current solutions address OKB canonicalization by devising advanced clustering algorithms and using knowledge graph embedding (KGE) to further facilitate the canonicalization process. Nevertheless, these works fail to fully exploit the synergy between clustering and KGE learning, and the methods designed for these sub-tasks are sub-optimal. To this end, we put forward a multi-task learning framework, namely MulCanon, to tackle OKB canonicalization. Specifically, diffusion model is used in the soft clustering process to improve the noun phrase representations with neighboring information, which can lead to more accurate representations. MulCanon unifies the learning objective of diffusion model, KGE model, side information and cluster assignment, and adopts a two-stage multi-task learning paradigm for training. A thorough experimental study on popular OKB canonicalization benchmarks validates that MulCanon can achieve competitive canonicalization results.

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Funding

The authors would like to acknowledge the support provided by the Key R&D Program of Shandong Province, China (No. 2023CXGC010801), the National Natural Science Foundation of China (No.62302513 & 62272469), the “New 20 Regulations for Universities" funding program of Jinan (No.202228089) and the TaiShan Industrial Experts Programme (No.tscx202312128).

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Authors and Affiliations

  1. School of Software, Shandong University, 1500 Shunhua Road, Jinan, 250000, Shandong Province, China

    Bingchen Liu, Shijun Liu, Li Pan & Xin Li

  2. Laboratory for Big Data and Decision, National University of Defense Technology, 109 Deya Road, Changsha, 410073, Hunan Province, China

    Huang Peng, Weixin Zeng & Xiang Zhao

  3. QuanCheng Laboratory, Jinan, 250103, Shandong Province, China

    Shijun Liu

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  1. Bingchen Liu
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  7. Xin Li
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Contributions

Bingchen Liu wrote the main manuscript text, prepared all figures and tables and provided the methodology. Weixin Zeng, Xiang Zhao and Huang Peng provided writing-review and editing. Li Pan, Xin Li and Shijun Liu provided writing-review and editing and provided funding support.

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Correspondence to Weixin Zeng.

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Liu, B., Peng, H., Zeng, W. et al. Open knowledge base canonicalization with multi-task learning. World Wide Web 27, 51 (2024). https://doi.org/10.1007/s11280-024-01288-x

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