Dataset Card for "relbert/nell"

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.",
}