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Xiaobin Tang

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AAAI Conference 2022 Conference Paper

CODE: Contrastive Pre-training with Adversarial Fine-Tuning for Zero-Shot Expert Linking

  • Bo Chen
  • Jing Zhang
  • Xiaokang Zhang
  • Xiaobin Tang
  • lingfan cai
  • Hong Chen
  • Cuiping Li
  • Peng Zhang

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, and collaborators. However, its quality is suffered from lack of ample sources of expert information. This paper employs AMiner as the basis with an aim at linking any external experts to the counterparts on AMiner. As it is infeasible to acquire sufficient linkages from arbitrary external sources, we explore the problem of zero-shot expert linking. In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the model’s transferability in an adversarial manner. For evaluation, we first design two intrinsic tasks, author identification and paper clustering, to validate the representation and matching capability endowed by contrastive learning. Then the final external expert linking performance on two genres of external sources also implies the superiority of the adversarial fine-tuning method. Additionally, we show the online deployment of CODE, and continuously improve its online performance via active learning.

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IJCAI Conference 2020 Conference Paper

BERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment

  • Xiaobin Tang
  • Jing Zhang
  • Bo Chen
  • Yang Yang
  • Hong Chen
  • Cuiping Li

Knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1. 9-9. 7% in terms of HitRatio@1 on the dataset DBP15K.

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v2026.09.13