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
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2325–2331
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.283
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.lrec-1.283.pdf