Ziheng Zhang


2023

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Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue | Yongqi Zhang | Quanming Yao | Yong Li | Xian Wu | Ziheng Zhang | Zhenxi Lin | Yefeng Zheng
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.

2022

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Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang | Chi Liu | Nuwa Xi | Sendong Zhao | Meizhi Ju | Shiwei Zhang | Ziheng Zhang | Yefeng Zheng | Bing Qin | Ting Liu
Proceedings of the 29th International Conference on Computational Linguistics

Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.

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Multi-modal Contrastive Representation Learning for Entity Alignment
Zhenxi Lin | Ziheng Zhang | Meng Wang | Yinghui Shi | Xian Wu | Yefeng Zheng
Proceedings of the 29th International Conference on Computational Linguistics

Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities. Most previous works focus on how to utilize and encode information from different modalities, while it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. In this paper, we propose MCLEA, a Multi-modal Contrastive Learning based Entity Alignment model, to obtain effective joint representations for multi-modal entity alignment. Different from previous works, MCLEA considers task-oriented modality and models the inter-modal relationships for each entity representation. In particular, MCLEA firstly learns multiple individual representations from multiple modalities, and then performs contrastive learning to jointly model intra-modal and inter-modal interactions. Extensive experimental results show that MCLEA outperforms state-of-the-art baselines on public datasets under both supervised and unsupervised settings.

2021

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OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Yuejia Xiang | Ziheng Zhang | Jiaoyan Chen | Xi Chen | Zhenxi Lin | Yefeng Zheng
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng | Rui Wen | Xi Chen | Yifan Yang | Yunyan Zhang | Ziheng Zhang | Ningyu Zhang | Bin Qin | Xu Ming | Yefeng Zheng
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.

2020

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An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang | Hualuo Liu | Jiaoyan Chen | Xi Chen | Bo Liu | YueJia Xiang | Yefeng Zheng
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.