About: Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Creator	Korea, South Hwang, Sung Lee, Dong Kaist, Aitrics, »more»
source	ArXiv
abstract	Many practical graph problems, such as knowledge graph construction and drug-to-drug interaction, require to handle multi-relational graphs. However, handling real-world multi-label graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, where new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph link prediction and drug-to-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
has issue date	2020-06-11(xsd:dateTime)
has license	arxiv
sha1sum (hex)	225d571f97f9097be436626f225cff572007aaf4
resource representing a document's title	Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
resource representing a document's body	covid:225d571f97f9097be436626f225cff572007aaf4#body_text
is schema:about of	named entity 'edges' named entity 'tackle' named entity 'embedding' named entity 'nature' named entity 'Networks' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software