TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914431722586112 |
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| author | Nie, Feng Ye, Zhixiu Xie, Sifa Wu, Shuang Yuan, Xin Yao, Liang Peng, Jiazhen Cheng, Xu |
| author_facet | Nie, Feng Ye, Zhixiu Xie, Sifa Wu, Shuang Yuan, Xin Yao, Liang Peng, Jiazhen Cheng, Xu |
| contents | WikiKG90Mv2 in NeurIPS 2022 is a large encyclopedic knowledge graph. Embedding knowledge graphs into continuous vector spaces is important for many practical applications, such as knowledge acquisition, question answering, and recommendation systems. Compared to existing knowledge graphs, WikiKG90Mv2 is a large scale knowledge graph, which is composed of more than 90 millions of entities. Both efficiency and accuracy should be considered when building graph embedding models for knowledge graph at scale. To this end, we follow the retrieve then re-rank pipeline, and make novel modifications in both retrieval and re-ranking stage. Specifically, we propose a priority infilling retrieval model to obtain candidates that are structurally and semantically similar. Then we propose an ensemble based re-ranking model with neighbor enhanced representations to produce final link prediction results among retrieved candidates. Experimental results show that our proposed method outperforms existing baseline methods and improves MRR of validation set from 0.2342 to 0.2839. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28512 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC Nie, Feng Ye, Zhixiu Xie, Sifa Wu, Shuang Yuan, Xin Yao, Liang Peng, Jiazhen Cheng, Xu Computation and Language WikiKG90Mv2 in NeurIPS 2022 is a large encyclopedic knowledge graph. Embedding knowledge graphs into continuous vector spaces is important for many practical applications, such as knowledge acquisition, question answering, and recommendation systems. Compared to existing knowledge graphs, WikiKG90Mv2 is a large scale knowledge graph, which is composed of more than 90 millions of entities. Both efficiency and accuracy should be considered when building graph embedding models for knowledge graph at scale. To this end, we follow the retrieve then re-rank pipeline, and make novel modifications in both retrieval and re-ranking stage. Specifically, we propose a priority infilling retrieval model to obtain candidates that are structurally and semantically similar. Then we propose an ensemble based re-ranking model with neighbor enhanced representations to produce final link prediction results among retrieved candidates. Experimental results show that our proposed method outperforms existing baseline methods and improves MRR of validation set from 0.2342 to 0.2839. |
| title | TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.28512 |