Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-of-the-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.

该论文介绍了一种基于深度神经网络和语义知识图谱的深层语义相关性模型（DSRM），用于测量实体之间的语义相关性以进行话题连贯性建模，相比于现有方法，DSRM 在两个公开数据集上减少了19.4％和24.5％ 的实体消岐错误率。

利用深度神经网络和知识图谱进行实体消歧