Taxonomy enrichment with text and graph vector representations release_mspkmhpj2zgvza6ndi5zrxt5ca

by Irina Nikishina, Mikhail Tikhomirov, Varvara Logacheva, Yuriy Nazarov, Alexander Panchenko, Natalia Loukachevitch

Published in Semantic Web Journal by IOS Press.

2022   p1-35

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 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 which 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 node2vec, Poincaré embeddings, GCN 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 explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We 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.
In application/xml+jats format

Archived Files and Locations

application/pdf  581.3 kB
file_t3nwxcrzt5afrixtnhpizsb24y
content.iospress.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-02-03
Container Metadata
Open Access Publication
Not in DOAJ
In Keepers Registry
ISSN-L:  1570-0844
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: ea23af25-9c95-4536-9ec4-476e4d479d00
API URL: JSON