Hugging Face's logo

Datasets:
relbert
/
nell

Modalities:
Text
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
License:
Dataset card Viewer Files Community
nell / README.md
asahi417's picture
asahi417
fix readme
0fb6736
preview code
|
raw
history blame contribute delete
22 kB
metadata
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - n<1K
pretty_name: relbert/nell

Dataset Card for "relbert/nell"

Dataset Description

Dataset Summary

This is NELL-ONE dataset for the few-shots link prediction proposed in https://aclanthology.org/D18-1223/. Please see NELL paper to know more about the original dataset.

  • Number of instances
train validation test
number of pairs 5498 878 1352
number of unique relation types 32 4 6
  • Number of pairs in each relation type
number of pairs (train) number of pairs (validation) number of pairs (test)
concept:airportincity 210 0 0
concept:athleteledsportsteam 424 0 0
concept:automobilemakercardealersinstateorprovince 78 0 0
concept:bankboughtbank 58 0 0
concept:ceoof 271 0 0
concept:cityradiostation 99 0 0
concept:citytelevisionstation 316 0 0
concept:countriessuchascountries 100 0 0
concept:countrycapital 211 0 0
concept:countryhascitizen 182 0 0
concept:countryoforganizationheadquarters 166 0 0
concept:countrystates 169 0 0
concept:drugpossiblytreatsphysiologicalcondition 91 0 0
concept:fatherofperson 108 0 0
concept:fooddecreasestheriskofdisease 1 0 0
concept:hasofficeincountry 283 0 0
concept:leaguecoaches 71 0 0
concept:leaguestadiums 279 0 0
concept:musicartistmusician 118 0 0
concept:musicgenressuchasmusicgenres 107 0 0
concept:organizationnamehasacronym 61 0 0
concept:personalsoknownas 78 0 0
concept:personleadsgeopoliticalorganization 120 0 0
concept:personmovedtostateorprovince 225 0 0
concept:politicianrepresentslocation 258 0 0
concept:politicianusholdsoffice 216 0 0
concept:statehascapital 151 0 0
concept:stateorprovinceoforganizationheadquarters 118 0 0
concept:teamhomestadium 138 0 0
concept:teamplaysincity 338 0 0
concept:topmemberoforganization 354 0 0
concept:wifeof 99 0 0
concept:bankbankincountry 0 229 0
concept:cityalsoknownas 0 356 0
concept:parentofperson 0 217 0
concept:politicalgroupofpoliticianus 0 76 0
concept:automobilemakerdealersincity 0 0 177
concept:automobilemakerdealersincountry 0 0 96
concept:geopoliticallocationresidenceofpersion 0 0 143
concept:politicianusendorsespoliticianus 0 0 386
concept:producedby 0 0 209
concept:teamcoach 0 0 341
  • Number of entity types
head (train) tail (train) head (validation) tail (validation) head (test) tail (test)
actor 6 2 0 0 0 0
airport 152 0 0 0 0 0
astronaut 4 0 0 1 0 1
athlete 353 21 1 2 0 59
attraction 4 1 0 0 0 0
automobilemaker 131 29 0 0 273 54
bank 109 126 144 0 0 0
biotechcompany 14 80 0 0 0 10
building 4 0 0 0 0 0
celebrity 6 5 0 0 4 2
ceo 423 0 0 0 0 0
city 342 852 316 316 42 161
coach 29 61 0 3 0 245
comedian 1 0 0 0 0 0
company 76 549 1 0 1 144
country 755 455 0 197 27 91
county 36 39 11 11 10 4
creditunion 1 0 0 0 0 0
criminal 3 0 1 0 0 1
director 2 0 0 0 0 1
drug 91 0 0 0 1 0
female 116 8 38 9 3 3
geopoliticallocation 184 112 96 29 24 8
geopoliticalorganization 28 68 8 21 1 7
governmentorganization 25 95 74 0 0 0
island 15 4 4 6 1 0
journalist 4 0 0 0 0 1
male 132 78 37 52 1 5
model 2 0 0 0 0 0
monarch 4 3 4 1 0 0
museum 1 5 0 0 0 0
musicartist 118 5 0 0 0 0
musicgenre 107 107 0 0 0 0
musician 5 124 0 0 0 0
newspaper 3 2 0 0 0 0
organization 23 86 1 1 32 2
person 350 256 116 131 0 96
personafrica 1 3 0 0 0 0
personasia 1 3 0 0 0 0
personaustralia 38 5 0 0 0 5
personcanada 19 14 0 0 0 0
personeurope 9 7 14 4 0 1
personmexico 57 14 0 0 0 20
personnorthamerica 9 6 0 0 0 3
personsouthamerica 1 1 0 17 0 0
personus 41 21 2 0 1 6
planet 1 0 0 0 0 1
politician 107 5 0 1 23 58
politicianus 408 12 3 71 352 360
politicsblog 2 3 0 0 0 0
port 7 0 0 0 0 0
professor 7 2 0 0 1 0
publication 1 21 0 0 0 0
recordlabel 1 13 0 0 0 0
retailstore 1 15 0 0 0 0
school 54 1 0 0 11 0
scientist 5 2 0 1 0 0
sportsleague 356 12 0 0 0 0
sportsteam 392 430 0 0 295 0
stateorprovince 254 602 0 0 38 0
transportation 36 2 0 0 0 0
university 3 15 0 0 0 0
visualizablescene 20 7 3 3 3 3
visualizablething 1 1 1 1 0 0
website 7 31 0 0 0 0
caf_ 0 1 0 0 0 0
continent 0 1 0 0 0 0
disease 0 92 0 0 0 0
hotel 0 1 0 0 0 0
magazine 0 5 0 0 0 0
nongovorganization 0 4 0 0 0 0
nonprofitorganization 0 2 0 0 0 0
park 0 1 0 0 0 0
petroleumrefiningcompany 0 6 0 0 0 0
politicaloffice 0 216 0 0 0 0
politicalparty 0 6 2 0 0 0
radiostation 0 93 0 0 0 0
river 0 4 0 0 0 0
stadiumoreventvenue 0 417 0 0 0 0
televisionnetwork 0 1 0 0 0 0
televisionstation 0 221 0 0 0 0
trainstation 0 2 0 0 0 0
writer 0 3 1 0 0 0
zoo 0 1 0 0 0 0
automobilemodel 0 0 0 0 100 0
product 0 0 0 0 62 0
software 0 0 0 0 42 0
videogame 0 0 0 0 4 0

Dataset Structure

An example of test looks as below.

{
  "relation": "concept:producedby",
  "head": "Toyota Tacoma",
  "head_type": "automobilemodel",
  "tail": "Toyota",
  "tail_type": "automobilemaker"
}

Citation Information 

@inproceedings{xiong-etal-2018-one,
    title = "One-Shot Relational Learning for Knowledge Graphs",
    author = "Xiong, Wenhan  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1223",
    doi = "10.18653/v1/D18-1223",
    pages = "1980--1990",
    abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}