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.
@article{arxiv.1808.07724,
title = {Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs},
author = {Dimitri Kartsaklis and Mohammad Taher Pilehvar and Nigel Collier},
journal= {arXiv preprint arXiv:1808.07724},
year = {2018}
}
Comments
Accepted for presentation at EMNLP 2018 (main conference)