Abstract
A Knowledge Graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framework, namely MCapsEED (Multi-Scale Capsule-based Embedding Model Incorporating Entity Descriptions). MCapsEED employs a Transformer in combination with a relation attention mechanism to identify the relation-specific part of an entity description and obtain the description representation of an entity. The structured and description representations of an entity are integrated into a synthetic representation. A 3-column matrix with each column a synthetic representation of an element of a triple is fed into a Multi-Scale Capsule-based Embedding model to produce final representations of the head entity, the tail entity and the relation. Experiments show that MCapsEED achieves better performance than state-of-the-art embedding models for the task of link prediction on four benchmark datasets. Our code can be found at https://github.com/1780041410/McapsEED.
Highlights
A Knowledge Graph (KG) is a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities [1]
We propose a novel KG representation learning (KGRL) framework, which takes advantage of both structured information and entity descriptions. We name it as Multi-Scale Capsule-based Embedding model incorporating Entity Descriptions (MCapsEED)
It can be drawn that, by incorporating entity description information, the performance of the MCapsEED is significantly better than MCapsE and CapsE, especially in the metrics of Hits@10, which indicates that MCapsEED further improve the discrimination of entity representation
Summary
A Knowledge Graph (KG) is a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities [1]. The inherent shortcoming of translation-based models still exists, as shallow networks cannot adequately extract relevant features of entities and relations. To further improve the performance of KGRL, there have been substantial works on incorporating additional information, e.g., entity types, relation paths, and entity descriptions. We propose a novel KGRL framework, which takes advantage of both structured information and entity descriptions. We name it as Multi-Scale Capsule-based Embedding model incorporating Entity Descriptions (MCapsEED). We propose a novel KG embedding framework MCapsEED which exploits both structured and entity description information. Comparing with KGRL models incorporating entity descriptions on FB15k and WN18 datasets, MCapsEED performs better on the MR and Hits@10 metrics.
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