Machine Learning with Knowledge Graphs

Volker Tresp gave a nice keynote talk at ESWC 2014 on leveraging the matrix factorization approach for prediction tasks on knowledge graphs (or ontologies, taxonomies, thesauri, RDF graphs, Web of Data, Linked Open Data... you name it).

Because I've been involved in the topic since 2012, for those who are interested in RESCAL model (the main avenue of his research group) and related work, I can complement his talk with a few (new and old) resources, he did not mention.

RESCAL Extensions

Exploiting type constraints while factorization:

[1] D. Krompass, M. Nickel, V. Tresp. Large-Scale Factorization of Type-Constrained Multi-Relational Data. 2014 [PDF]

[2] C. Kai-Wei, W. Yih, B. Yang, C. Meek. Typed Tensor Decomposition of Knowledge Bases for Relation Extraction // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013). - 2014 [PDF]

Using RESCAL in information retrieval (entity search), i.e., my visiting project at Emory University.

[3] N. Zhiltsov, E. Agichtein. Improving Entity Search over Linked Data by Modeling Latent Semantics. Proceedings of the International Conference on Information and Knowledge Management (CIKM 2013). - 2013 [PDF]

RESCAL Implementations

Original implementation:

[1] scikit-tensor. https://github.com/mnick/scikit-tensor, 2013

RESCAL in sparse matrices and coupled tensor-matrix factorization:

[2] Ext-RESCAL. https://github.com/nzhiltsov/Ext-RESCAL, 2012

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