Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

Understand

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.