Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

by   Gilles Vandewiele, et al.

As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques. RDF2Vec is an unsupervised technique that can create task-agnostic numerical representations of the nodes in a KG by extending successful language modelling techniques. The original work proposed the Weisfeiler-Lehman (WL) kernel to improve the quality of the representations. However, in this work, we show both formally and empirically that the WL kernel does little to improve walk embeddings in the context of a single KG. As an alternative to the WL kernel, we propose five different strategies to extract information complementary to basic random walks. We compare these walks on several benchmark datasets to show that the n-gram strategy performs best on average on node classification tasks and that tuning the walk strategy can result in improved predictive performances.



page 1

page 2

page 3

page 4


Walk this Way! Entity Walks and Property Walks for RDF2vec

RDF2vec is a knowledge graph embedding mechanism which first extracts se...

BiasedWalk: Learning Global-aware Node Embeddings via Biased Sampling

Popular node embedding methods such as DeepWalk follow the paradigm of p...

STWalk: Learning Trajectory Representations in Temporal Graphs

Analyzing the temporal behavior of nodes in time-varying graphs is usefu...

Learning Representations using Spectral-Biased Random Walks on Graphs

Several state-of-the-art neural graph embedding methods are based on sho...

On Homotopy of Walks and Spherical Maps in Homotopy Type Theory

We work with combinatorial maps to represent graph embeddings into surfa...

LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space

For graph classification tasks, many methods use a common strategy to ag...

Putting RDF2vec in Order

The RDF2vec method for creating node embeddings on knowledge graphs is b...
This week in AI

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