Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs
Dimitri Kartsaklis, Mohammad Taher Pilehvar, Nigel Collier · 2018
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 largescale text-to-entity mapping and entity classification tasks, with state of the art results.* This paper is dedicated to the memory of Euripides Kartsaklis, a man who loved technology.