892 citations · 2.3k across the 60 of their papers we have counts for
4 papers · 1 filter
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To t…
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen +2
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…
A Capsule Network-based Embedding Model for Search Personalization
Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen +1
Search personalization aims to tailor search results to each specific user based on the user's personal interests and preferences (i.e., the user profile). Recent research approach…
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen +1
In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neura…