针对大规模知识图谱的链接预测，提出检索-重排新方法，提升准确率。
TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
- 采用先检索后重排的流程，优先筛选结构语义相似的候选实体
- 在验证集上将MRR从0.2342提升至0.2839，显著提高预测精度
- 适合需要高精度链接预测的大规模知识图谱应用场景
NeurIPS 2022中的WikiKG90Mv2是一个大规模百科知识图谱，包含超过9000万个实体。将知识图谱嵌入连续向量空间对知识获取、问答系统和推荐等应用至关重要。面对如此大规模的数据，模型需兼顾效率与准确性。为此，本文采用“检索-重排”流程，并在两阶段进行创新：首先提出优先补全检索模型，筛选出在结构和语义上均相似的候选实体；随后设计基于集成的重排模型，引入邻域增强表示，对候选结果进行精细化排序。实验表明，该方法优于现有基线，在验证集上的MRR从0.2342提升至0.2839。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。