Taxonomy Enrichment with Text and Graph Vector Representations

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym ("is-a") relationship. Hypo-hypernymy is presented in almost every knowledge base and is used to describe the order of thing we live by. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy. Deep representations of graph structures like GCN autoencoder, Poincaré embeddings, node2vec emerged and have recently demonstrated very promising results on various NLP tasks. Our approach is a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We achieve state-of-the-art results across different datasets.