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:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.283.pdf
Export citation
@inproceedings{faralli-etal-2020-multiple, title = "Multiple Knowledge {G}raph{DB} ({MKGDB})", author = "Faralli, Stefano and Velardi, Paola and Yusifli, Farid", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.283", pages = "2325--2331", 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.", language = "English", ISBN = "979-10-95546-34-4", }
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%0 Conference Proceedings %T Multiple Knowledge GraphDB (MKGDB) %A Faralli, Stefano %A Velardi, Paola %A Yusifli, Farid %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth Language Resources and Evaluation Conference %D 2020 %8 May %I European Language Resources Association %C Marseille, France %@ 979-10-95546-34-4 %G English %F faralli-etal-2020-multiple %X 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. %U https://aclanthology.org/2020.lrec-1.283 %P 2325-2331
Markdown (Informal)
[Multiple Knowledge GraphDB (MKGDB)](https://aclanthology.org/2020.lrec-1.283) (Faralli et al., LREC 2020)
- Multiple Knowledge GraphDB (MKGDB) (Faralli et al., LREC 2020)
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.