A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

Abstract

In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.

Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths

“…-ConvKB [22] improves ConvE by taking the transitional characteristic (i.e., one of the most useful intuitions for knowledge graph completion) into consideration. -CapsE [23] combines convolutional neural network with capsule network [29] for knowledge graph embedding.…”

“…The best score is in bold, while the second best score is in underline. For comparison methods, the values in black color are the results listed in the original publication, except Con-vKB uses the [23] implemented version, which has been reported significantly better performance than the original one. The values in blue color are obtained by implementations from the OpenKE repository.…”

“…Various methods have been proposed for knowledge graph embedding, such as general linear-based models [6], bilinear-based models [13,27,34], translationbased models [5,9,14,15,20,41,43], and neural network-based models [4,7,22,23,[31][32][33]. We refer to [24,39] for a recent survey.…”

“…Recently, several convolutional neural network (CNN)-based models [7,22,23] have been proposed to learn the embeddings of entities and relations in knowledge graphs, in which [22] reserves the transitional characteristic in translationbased models and is comparably simple and efficient, achieves state-of-the-art performance. However, it only focuses on knowledge triples, ignoring the rich knowledge contained in relation paths.…”

Candidate gene prioritization using graph embedding

“…Moreover, ConvKB, introduced by Nguyen et al [31], is a CNN based model for knowledge graph completion which successfully achieved state-of-the-art results. In practical, knowledge graph embedding models are commonly constructed to model entries at the same dimension for the given triple, where presumably each dimension captures some relation-specific attribute of entities [32]. However, following the research of [32], existing models are not available to provide a deep architecture to model the relationship among entities in triples on the same dimension.…”