Multiple Knowledge GraphDB (MKGDB)

Stefano Faralli, Paola Velardi, Farid Yusifli


Abstract
We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy. MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others. Our resource consists of a large hypernymy graph which counts more than 37 million nodes and more than 81 million hypernymy relations.
Anthology ID:
2020.lrec-1.283
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2325–2331
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.283
DOI:
Bibkey:
Cite (ACL):
Stefano Faralli, Paola Velardi, and Farid Yusifli. 2020. Multiple Knowledge GraphDB (MKGDB). In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2325–2331, Marseille, France. European Language Resources Association.
Cite (Informal):
Multiple Knowledge GraphDB (MKGDB) (Faralli et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.lrec-1.283.pdf