Convolutional Neural Knowledge Graph Learning

10/23/2017
by   Feipeng Zhao, et al.
0

Previous models for learning entity and relationship embeddings of knowledge graphs such as TransE, TransH, and TransR aim to explore new links based on learned representations. However, these models interpret relationships as simple translations on entity embeddings. In this paper, we try to learn more complex connections between entities and relationships. In particular, we use a Convolutional Neural Network (CNN) to learn entity and relationship representations in knowledge graphs. In our model, we treat entities and relationships as one-dimensional numerical sequences with the same length. After that, we combine each triplet of head, relationship, and tail together as a matrix with height 3. CNN is applied to the triplets to get confidence scores. Positive and manually corrupted negative triplets are used to train the embeddings and the CNN model simultaneously. Experimental results on public benchmark datasets show that the proposed model outperforms state-of-the-art models on exploring unseen relationships, which proves that CNN is effective to learn complex interactive patterns between entities and relationships.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

02/10/2022

InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities

Knowledge graph embedding (KGE) models learn the representation of entit...
03/29/2021

Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web

Knowledge is captured in the form of entities and their relationships an...
02/03/2018

Incorporating Literals into Knowledge Graph Embeddings

Knowledge graphs, on top of entities and their relationships, contain an...
05/25/2019

MDE: Multi Distance Embeddings for Link Prediction in Knowledge Graphs

Over the past decade, knowledge graphs became popular for capturing stru...
10/27/2021

Standing on the Shoulders of Predecessors: Meta-Knowledge Transfer for Knowledge Graphs

Knowledge graphs (KGs) have become widespread, and various knowledge gra...
11/15/2019

CNN-based Dual-Chain Models for Knowledge Graph Learning

Knowledge graph learning plays a critical role in integrating domain spe...
12/07/2016

Knowledge Representation in Graphs using Convolutional Neural Networks

Knowledge Graphs (KG) constitute a flexible representation of complex re...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.