NOTE：KDD-CUP 2021 WikiKG90M-LSC解决方案
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC
摘要：
KDD Cup 2021中的WikiKG90 M是一个大型的知识图谱，它可以使各种下游应用受益，如问答和推荐系统。邀请参与者通过预测缺失的三胞胎来完成知识图谱。最近的表示学习方法在FB15 k-237等标准数据集上取得了巨大成功。因此，我们训练不同领域的先进算法来学习三元组，包括OTE，QuatE，RotatE和TransE。值得注意的是，我们将OTE修改为NOTE（Norm-OTE的缩写）以获得更好的性能。此外，我们使用DeepWalk和后平滑技术来捕获图结构以进行补充。除了表示之外，我们还使用头部实体，关系和尾部实体之间的各种统计概率进行最终预测。实验结果表明，最先进的表示学习方法的集成可以借鉴彼此的优势。我们从验证候选人中开发特征工程，以进一步改进。请注意，我们对测试集应用相同的策略以进行最终推断。当考虑对所有实体进行排名时，这些功能在现实世界中可能不实用。
英文摘要：
WikiKG90M in KDD Cup 2021 is a large encyclopedic knowledge graph, which could benefit various downstream applications such as question answering and recommender systems. Participants are invited to complete the knowledge graph by predicting missing triplets. Recent representation learning methods have achieved great success on standard datasets like FB15k-237. Thus, we train the advanced algorithms in different domains to learn the triplets, including OTE, QuatE, RotatE and TransE. Significantly, we modified OTE into NOTE (short for Norm-OTE) for better performance. Besides, we use both the DeepWalk and the post-smoothing technique to capture the graph structure for supplementation. In addition to the representations, we also use various statistical probabilities among the head entities, the relations and the tail entities for the final prediction. Experimental results show that the ensemble of state-of-the-art representation learning methods could draw on each others strengths. And we develop feature engineering from validation candidates for further improvements. Please note that we apply the same strategy on the test set for final inference. And these features may not be practical in the real world when considering ranking against all the entities.
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。
