  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.
