Relation-based multi-type aware knowledge graph embedding

Abstract

Listen

Translate article

Knowledge graph (KG) embedding projects the graph into a low-dimensional space and preserves the graph information. An essential part of a KG is the ontology, which always is organized as a taxonomy tree, depicting the type (or multiple types) of each entity and the hierarchical relationships among these types. The importance of considering the ontology during KG embedding lies in its ability to provide side-information, improving the downstream applications’ accuracy (e.g., link prediction, entity alignment or recommendation). However, the ontology has yet to receive adequate attention during the KG embedding, especially for instances where each entity may belong to multiple types. This ontology-enhanced KG embedding’s main challenges are twofold: determining how to discover the relationships among these types and how to integrate them with the entities’ relationship network. Although it is common to see attention-based models used in KG embedding, they cannot settle the issues raised simultaneously. Only a single type is assigned to each entity and the correlation among types are ignored in those models, leading to information loss and encumbered downstream tasks. To overcome these challenges, we propose a composite multi-type aware KG embedding model, whose main components are a multi-type layer and entity embedding layer. We model it as a natural language processing task at the multi-type layer to discover each entity’s multi-type feature and automatically capture their correlations. Additionally, a relation-based attention mechanism is conducted at the entity embedding layer, which aggregates neighborhoods’ information and integrates the multi-type layer’s information through common entities of these two layers. Through extensive experiments on two real KGs, we demonstrate that, compared to several state-of-the-art baselines, our Multi-Type aware Embedding (MTE) model achieves substantial gain in both Mean Rank and Hit@N for the link prediction task and accuracy for multi-type classification.