Node Embeddings for Graph Merging: Case of Knowledge Graph Construction

Ida Szubert, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or incorrect failure to merge. We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora. AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity. We explore graph-based and word-based embedding models and show an overall error reduction of from 56% to 23.6%, with a reduction of over a half in both types of incorrect node matching.
Original languageEnglish
Title of host publicationProceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Place of PublicationHong Kong
PublisherAssociation for Computational Linguistics
Pages172-176
Number of pages5
ISBN (Electronic)978-1-950737-86-4
DOIs
Publication statusPublished - 4 Nov 2019
EventTextGraphs: The 13th Workshop on Graph-based Methods for Natural Language Processing - , Hong Kong
Duration: 4 Nov 20194 Nov 2019
https://sites.google.com/view/textgraphs2019

Workshop

WorkshopTextGraphs
Abbreviated titleTextGraphs 2019
CountryHong Kong
Period4/11/194/11/19
Internet address

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