NeurIPS 2022 WikiKG90Mv2-LSC 的 TIEG-Youpu 解决方案
TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
AI总结：
本文提出 TIEG-Youpu 方法，通过检索与重排序流程提升大规模知识图谱嵌入效果，实验显示在验证集上 MRR 从 0.2342 提升至 0.2839。
摘要：
WikiKG90Mv2 是 NeurIPS 2022 的大规模百科知识图谱。将知识图谱嵌入连续向量空间对许多实际应用至关重要，如知识获取、问答和推荐系统。与现有知识图谱相比，WikiKG90Mv2 是大规模的，包含超过 9000 万个实体。在构建大规模知识图谱的图嵌入模型时，效率和准确性都应考虑。为此，我们遵循检索然后重排序流程，并在检索和重排序阶段进行创新修改。具体而言，我们提出了一种优先填充检索模型以获取在结构和语义上相似的候选。然后我们提出了一种基于集成的重排序模型，具有邻居增强的表示，以生成检索候选之间的最终链接预测结果。实验结果表明，我们提出的方法优于现有基线方法，并将验证集的 MRR 从 0.2342 提高到 0.2839。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
