Knowledge Graph and Text Jointly Embedding (2014) Zhen Wang yx...(About) method of jointly embedding knowledge graphs and a text corpus so that entities and words/phrases are represented in the same vector space.
Promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text (in particular, enables the prediction of facts containing entities out of the knowledge graph)
[cité par J. Moreno](/doc/?uri=https%3A%2F%2Fhal.archives-ouvertes.fr%2Fhal-01626196%2Fdocument)
Traversing Knowledge Graphs in Vector Space (2015)(About) Knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new "compositional" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy.