Linking textual content to entities from the knowledge graph has received increasing attention in the context of which surface form representations of entities, e.g., terms or phrases, are disambiguated and linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted and reasoned on at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing entity linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. In this paper, we investigate how implicit entities within social content can be identified and linked. The contributions of our work include (1) modeling the problem of implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet, (2) the introduction and systematic classification of appropriate features for identifying implicit entities, (3) extensive evaluation of the proposed approach in comparison with existing state of the art as well as performing feature analysis over proposed features, and (4) the qualitative assessment of the root causes for mislabeled instances in our experiments and careful discussion on how mislabeled entity links can be addressed as a part of future work. In our experiments, we show that our proposed features are able to improve the state of the art over the standard Precision at 1 (P@1) metric.

在知识的实体的表面形式表示（例如术语或短语）被消除歧义并链接到适当的实体的情况下，将文本内容从知识图中链接到实体受到了越来越多的关注。这允许文本内容（例如，社交用户生成的内容）在更高的语义级别上被解释和推理。但是，最近的研究表明，至少15％的社交用户生成的内容没有他们讨论的实体的明确表面形式表示。换句话说，仅暗示内容的主题。对于这种情况，由于缺少实体表面形式，因此现有的实体链接方法（称为显式实体链接）无法执行链接。在本文中，我们研究了如何识别和链接社交内容中的隐式实体。我们的工作包括（1）将隐式实体链接问题建模为学习排名问题，其中知识图实体基于其与输入推文的相关性进行排名；（2）引入和系统分类适当的特征以进行识别隐式实体，（3）与现有技术相比，对所提出方法的广泛评估，以及对所提出特征进行特征分析，以及（4）在实验和仔细讨论中对错误标记实例的根本原因进行定性评估关于如何将标记错误的实体链接作为未来工作的一部分进行处理。在我们的实验中，我们表明，我们提出的功能能够改善标准1级（P @ 1）精度下的技术水平。