Node Embeddings for Graph Merging: Case of Knowledge Graph Construction

Ida Szubert, Mark Steedman


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.
Anthology ID:
D19-5321
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
172–176
Language:
URL:
https://aclanthology.org/D19-5321
DOI:
10.18653/v1/D19-5321
Bibkey:
Cite (ACL):
Ida Szubert and Mark Steedman. 2019. Node Embeddings for Graph Merging: Case of Knowledge Graph Construction. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 172–176, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction (Szubert & Steedman, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5321.pdf