  This paper addresses the problem of mapping natural language text to
knowledge base entities. The mapping process is approached as a composition of
a phrase or a sentence into a point in a multi-dimensional entity space
obtained from a knowledge graph. The compositional model is an LSTM equipped
with a dynamic disambiguation mechanism on the input word embeddings (a
Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base
space is prepared by collecting random walks from a graph enhanced with textual
features, which act as a set of semantic bridges between text and knowledge
base entities. The ideas of this work are demonstrated on large-scale
text-to-entity mapping and entity classification tasks, with state of the art
results.
