On embeddings as an alternative paradigm for relational learning

06/29/2018
by   Sebastijan Dumančić, et al.
0

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.

READ FULL TEXT
research
06/29/2018

On embeddings as alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...
research
07/04/2020

Nested Subspace Arrangement for Representation of Relational Data

Studies on acquiring appropriate continuous representations of discrete ...
research
02/19/2020

Error detection in Knowledge Graphs: Path Ranking, Embeddings or both?

This paper attempts to compare and combine different approaches for de-t...
research
05/05/2020

Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases

The recent developments and growing interest in neural-symbolic models h...
research
05/27/2019

Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey

Graphs arise naturally in many real-world applications including social ...
research
11/09/2018

Reasoning over RDF Knowledge Bases using Deep Learning

Semantic Web knowledge representation standards, and in particular RDF a...
research
09/17/2017

On Inductive Abilities of Latent Factor Models for Relational Learning

Latent factor models are increasingly popular for modeling multi-relatio...

Please sign up or login with your details

Forgot password? Click here to reset