Putting RDF2vec in Order

08/11/2021
by   Jan Portisch, et al.
0

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue that this might be a shortcoming when training RDF2vec, and show that using a word2vec variant which respects order yields considerable performance gains especially on tasks where entities of different classes are involved.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/15/2021

EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs

Entity Resolution (ER) is a constitutional part for integrating differen...
research
03/30/2017

Efficient Parallel Translating Embedding For Knowledge Graphs

Knowledge graph embedding aims to embed entities and relations of knowle...
research
05/17/2021

Federated Knowledge Graphs Embedding

In this paper, we propose a novel decentralized scalable learning framew...
research
09/12/2022

Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces

Recently there is an increasing scholarly interest in time-varying knowl...
research
10/22/2021

Creating Knowledge Graphs Subsets using Shape Expressions

The initial adoption of knowledge graphs by Google and later by big comp...
research
07/20/2023

Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs

Knowledge graphs (KGs) have become the standard technology for the repre...
research
09/09/2020

Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

As KGs are symbolic constructs, specialized techniques have to be applie...

Please sign up or login with your details

Forgot password? Click here to reset