RDF2Vec: RDF Graph Embeddings and Their Applications

Tracking #: 1738-2950

Petar Ristoski
Jessica Rosati
Tommaso Di Noia
Renato De Leone
Heiko Paulheim

Responsible editor: 
Freddy Lecue

Submission type: 
Full Paper
Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs.We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs.We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
Full PDF Version: 


Solicited Reviews:
Click to Expand/Collapse
Review #1
By Achim Rettinger submitted on 09/Jan/2018
Review Comment:

Thanks for the rebuttal.

Review #2
Anonymous submitted on 17/Jan/2018
Review Comment:

This manuscript is a revision of manuscript #1643, which I had reviewed as Reviewer #2. Therefore, the following is just a follow-up review of this revision in comparison with the previous version of the manuscript.

I have read the new version of the manuscript and the answers provided by the authors and I am happy with the improvements they made.
I believe the manuscript can now be accepted for publication.

Review #3
By Jiewen Wu submitted on 31/Jan/2018
Review Comment:

I have reviewed the previous versions and all the comments have been addressed in this version. I recommend acceptance after reviewing the changes.