DisambiguART: A Neural-based Inference Model for Knowledge Graph Disambiguation

One main challenge in constructing a Knowledge Graph (KG) is to deal with ambiguity. Specifically, an entity in the graph can be assigned with multiple meanings while two or more entities considered to have different meanings may actually be the same. Assigning an entity with the correct meaning may involve re-evaluation of its relevant contexts. This costly operation typically involves searching for other similar entities within the KG such that the context can be determined. In this article, a new model called DisambiguART is proposed leveraging multi-channel matching and inference in a self-organizing neural network for sense disambiguation in KGs. Unlike other disambiguation methods that rely on representation learning to identify the relevant contexts whereby similarities among entities are learned, DisambiguART extends the working principle of multi-channel Adaptive Resonance Theory (ART) to conduct inferences directly over the graph representation through bi-directional interactions of bottom-up activations and top-down matching to find similar entities and select the correct meaning according to the right context. The proposed method is evaluated on the tasks of entity sense disambiguation in three domain KGs (jet engine, biomedical, and kinship) and author name disambiguation in bibliographic KGs, demonstrating the effectiveness and efficiency of DisambiguART against the state-of-the-art methods.