Taxonomy Enrichment with Text and Graph Vector Representations

Tracking #: 2847-4061

This paper is currently under review
Authors: 
Irina Nikishina
Mikhail Tikhomirov
Varvara Logacheva
Yuriy Nazarov
Alexander Panchenko
Natalia Loukachevitch

Responsible editor: 
Guest Editors DeepL4KGs 2021

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Full Paper
Abstract: 
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 the hypo-hypernym ("class-subclass") relationship. 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. We present a new method that allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like GCN, Poincaré embeddings, node2vec etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art. We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.
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