arg1
stringlengths
1
43
arg2
stringlengths
1
60
score
float64
1
1
wikipedia_primary_page
sequence
synset
sequence
snowdrop
carpel
0.999075
[ "Galanthus" ]
[ "wn.carpel.n.01" ]
plant
stem
0.99918
[ "Plant" ]
[ "wn.plant.n.02", "wn.stalk.n.02" ]
soy food
dietary fiber
0.998896
[ "Dietary fiber" ]
[]
hagfish
heart
0.999295
[ "Hagfish", "Heart" ]
[ "wn.hagfish.n.01", "wn.heart.n.01" ]
capillary hemangioma
vessel
0.999185
[ "Capillary hemangioma" ]
[ "wn.vessel.n.01" ]
bird
feather
0.999124
[ "Bird", "Feather" ]
[ "wn.bird.n.01", "wn.feather.n.01" ]
insect
respiratory system
0.999071
[ "Insect", "Respiratory system" ]
[ "wn.insect.n.01", "wn.respiratory_system.n.01" ]
surface
material
0.999239
[ "Surface", "Material" ]
[ "wn.surface.n.01", "wn.material.n.01" ]
fungus
flagella
0.999125
[ "Fungus" ]
[ "wn.fungus.n.01" ]
spiny mouse
scale tail
0.999399
[ "Spiny mouse", "Tail" ]
[ "wn.tail.n.01" ]
mammal
scale tail
0.999392
[ "Mammal", "Tail" ]
[ "wn.mammal.n.01", "wn.tail.n.01" ]
vascular plant
tissue
0.999304
[ "Vascular plant" ]
[ "wn.vascular_plant.n.01", "wn.tissue.n.01" ]
swan
plumage
0.99929
[ "Swan", "Plumage" ]
[ "wn.swan.n.01", "wn.feather.n.01" ]
leave
chlorophyll
0.999231
[ "Leaf", "Chlorophyll" ]
[ "wn.leaf.n.01", "wn.chlorophyll.n.01" ]
beak echidna
tongue
0.999363
[ "Tongue" ]
[ "wn.tongue.n.01" ]
oryx
sense
0.998863
[ "Oryx", "Sense" ]
[ "wn.oryx.n.01", "wn.sense.n.03" ]
food chain
nitrogen
0.998691
[ "Food chain", "Nitrogen" ]
[ "wn.food_chain.n.01", "wn.nitrogen.n.01" ]
cnidarian
tentacle
0.999114
[ "Tentacle" ]
[ "wn.coelenterate.n.01", "wn.tentacle.n.02" ]
cnidarian
cell
0.999187
[]
[ "wn.coelenterate.n.01", "wn.cell.n.02" ]
tentacle
cell
0.998625
[ "Tentacle" ]
[ "wn.tentacle.n.02", "wn.cell.n.02" ]
seed
enzyme
0.998944
[ "Seed", "Enzyme" ]
[ "wn.seed.n.01", "wn.enzyme.n.01" ]
geranium
flower
0.999327
[ "Geranium", "Flower" ]
[ "wn.geranium.n.01", "wn.flower.n.01" ]
bear
coat
0.999115
[ "Bear", "Coat" ]
[ "wn.bear.n.01" ]
wasp
wing
0.999061
[ "Wasp", "Wing" ]
[ "wn.wasp.n.02", "wn.wing.n.01" ]
hippos
and body
0.999302
[ "Hippopotamus" ]
[ "wn.hippopotamus.n.01", "wn.body.n.01" ]
hippos
skin
0.998924
[ "Skin" ]
[ "wn.hippopotamus.n.01", "wn.hide.n.02" ]
hippos
leg
0.999355
[ "Hippopotamus", "Leg" ]
[ "wn.hippopotamus.n.01", "wn.leg.n.01" ]
human
two nigrae
0.999102
[ "Human" ]
[ "wn.homo.n.02" ]
cheese
protein
0.99869
[ "Cheese", "Protein" ]
[ "wn.cheese.n.01", "wn.protein.n.01" ]
cheese
component
0.998974
[ "Cheese" ]
[ "wn.cheese.n.01", "wn.part.n.01" ]
treehopper
leg
0.999192
[ "Treehopper", "Leg" ]
[ "wn.treehopper.n.01", "wn.leg.n.02" ]
arthropod
exoskeleton
0.999164
[ "Arthropod", "Exoskeleton" ]
[ "wn.arthropod.n.01", "wn.exoskeleton.n.01" ]
animal
exoskeleton
0.999266
[ "Animal", "Exoskeleton" ]
[ "wn.animal.n.01", "wn.exoskeleton.n.01" ]
arthropod
body
0.999121
[ "Arthropod" ]
[ "wn.arthropod.n.01", "wn.body.n.01" ]
animal
body
0.999153
[ "Animal" ]
[ "wn.animal.n.01", "wn.body.n.01" ]
arthropod
appendage
0.999205
[ "Arthropod", "Appendage" ]
[ "wn.arthropod.n.01", "wn.extremity.n.01" ]
animal
appendage
0.999235
[ "Animal", "Appendage" ]
[ "wn.animal.n.01", "wn.extremity.n.01" ]
soil
root
0.998863
[ "Soil", "Root" ]
[ "wn.soil.n.02", "wn.root.n.01" ]
beta cell
proinsulin
0.999196
[ "Beta cell", "Proinsulin" ]
[ "wn.beta_cell.n.01" ]
mangrove
stem
0.999245
[ "Mangrove" ]
[ "wn.mangrove.n.01", "wn.stalk.n.02" ]
mangrove
leaf
0.998606
[ "Mangrove", "Leaf" ]
[ "wn.mangrove.n.01", "wn.leaf.n.01" ]
plant
leaf
0.999024
[ "Plant", "Leaf" ]
[ "wn.plant.n.02", "wn.leaf.n.01" ]
mangrove
root
0.998917
[ "Mangrove", "Root" ]
[ "wn.mangrove.n.01", "wn.root.n.01" ]
plant
root
0.999024
[ "Plant", "Root" ]
[ "wn.plant.n.02" ]
basis
hydroxide ion
0.998817
[]
[ "wn.base.n.10", "wn.hydroxide_ion.n.01" ]
snowfall
ice needle
0.999035
[]
[ "wn.snow.n.01", "wn.ice_crystal.n.01" ]
white tiger
stripe
0.999282
[ "White tiger" ]
[ "wn.stripe.n.05" ]
food
fatty acid
0.99914
[ "Food", "Fatty acid" ]
[ "wn.food.n.01", "wn.fatty_acid.n.01" ]
knee
hip socket
0.999253
[ "Knee" ]
[ "wn.knee.n.01", "wn.hip_socket.n.01" ]
cacti
flower
0.999164
[ "Cactus", "Flower" ]
[ "wn.cactus.n.01" ]
tyrannosaurus
brain
0.999342
[ "Tyrannosaurus", "Brain" ]
[ "wn.tyrannosaur.n.01", "wn.brain.n.01" ]
liver
metabolizing enzyme
0.998942
[ "Liver", "Enzyme" ]
[ "wn.liver.n.01", "wn.enzyme.n.01" ]
fungus
gall
0.998897
[ "Fungus", "Gall" ]
[ "wn.fungus.n.01", "wn.bile.n.01" ]
mollie
sperm
0.998802
[ "Sperm" ]
[ "wn.mollie.n.01", "wn.sperm.n.01" ]
shark
blood
0.998715
[ "Shark", "Blood" ]
[ "wn.shark.n.01", "wn.blood.n.01" ]
both the and digestive tract
mucus
0.999002
[ "Gastrointestinal tract", "Mucus" ]
[ "wn.alimentary_canal.n.01", "wn.mucus.n.01" ]
tom
bone cell
0.999185
[]
[ "wn.turkey_cock.n.01", "wn.bone_cell.n.01" ]
medicine
steroid
0.99905
[ "Medicine", "Steroid" ]
[ "wn.medicine.n.02", "wn.steroid.n.01" ]
animal
limb
0.999024
[ "Animal" ]
[ "wn.animal.n.01", "wn.limb.n.01" ]
dicotyledon
leave
0.998778
[ "Dicotyledon", "Leaf" ]
[ "wn.dicot.n.01", "wn.leaf.n.01" ]
dicotyledon
vein
0.999387
[ "Dicotyledon", "Vein" ]
[ "wn.dicot.n.01", "wn.vein.n.03" ]
leave
vein
0.999123
[ "Leaf", "Vein" ]
[ "wn.leaf.n.01", "wn.vein.n.03" ]
almond
alpha - linolenic acid
0.999105
[ "Almond", "Linolenic acid" ]
[ "wn.almond.n.02", "wn.linolenic_acid.n.01" ]
cacti
delicate white flower
0.999332
[ "Cactus", "Elaeocarpus bojeri" ]
[ "wn.cactus.n.01" ]
flower
pollen
0.998914
[ "Flower", "Pollen" ]
[ "wn.flower.n.01", "wn.pollen.n.01" ]
flower
seed capsule
0.999318
[ "Flower" ]
[ "wn.flower.n.01" ]
zygote
chromatin
0.999169
[ "Zygote", "Chromatin" ]
[ "wn.zygote.n.01", "wn.chromatin.n.01" ]
chicken
feather
0.999269
[ "Chicken", "Feather" ]
[ "wn.chicken.n.01", "wn.feather.n.01" ]
organism
tissue
0.99901
[ "Organism" ]
[ "wn.organism.n.01", "wn.tissue.n.01" ]
dandelion
flower
0.999403
[ "Taraxacum", "Flower" ]
[ "wn.taraxacum.n.01", "wn.flower.n.02" ]
capuchin
tail
0.999329
[ "Tail" ]
[ "wn.capuchin.n.02", "wn.tail.n.01" ]
animal
follicle
0.999159
[ "Animal" ]
[ "wn.animal.n.01", "wn.follicle.n.01" ]
animal
cell
0.998977
[ "Animal" ]
[ "wn.animal.n.01", "wn.cell.n.02" ]
sewage
organic matter
0.99904
[ "Sewage", "Organic matter" ]
[ "wn.sewage.n.01" ]
bundle sheath
chloroplast
0.999249
[ "Chloroplast" ]
[ "wn.chloroplast.n.01" ]
grasshopper
reproductive organ
0.999398
[ "Grasshopper", "Sex organ" ]
[ "wn.grasshopper.n.01", "wn.reproductive_organ.n.01" ]
oak ' flower part
catkin
0.998621
[ "Catkin" ]
[ "wn.part.n.03", "wn.catkin.n.01" ]
grease
group
0.999102
[]
[ "wn.grease.n.01", "wn.group.n.02" ]
cat
gland
0.99889
[ "Cat", "Gland" ]
[ "wn.cat.n.01", "wn.gland.n.01" ]
horse
cell
0.999127
[ "Horse" ]
[ "wn.horse.n.01", "wn.cell.n.02" ]
desert tortoise
dome shell
0.999309
[ "Desert tortoise" ]
[ "wn.desert_tortoise.n.01" ]
peccary
stomach
0.998818
[ "Peccary", "Stomach" ]
[ "wn.peccary.n.01", "wn.stomach.n.01" ]
peccary
four compartment
0.999163
[ "Peccary" ]
[ "wn.peccary.n.01", "wn.compartment.n.01" ]
stomach
four compartment
0.998807
[ "Stomach" ]
[ "wn.stomach.n.01", "wn.compartment.n.01" ]
bluefish
snout
0.999334
[ "Bluefish", "Snout" ]
[ "wn.bluefish.n.01", "wn.snout.n.01" ]
lynx
ring tail
0.999283
[ "Lynx", "Ring-tailed lemur" ]
[ "wn.lynx.n.02", "wn.madagascar_cat.n.01" ]
ant
sacs
0.999351
[ "Ant" ]
[ "wn.ant.n.01", "wn.sac.n.04" ]
gland
cortisol
0.998716
[ "Gland", "Cortisol" ]
[ "wn.gland.n.01", "wn.hydrocortisone.n.01" ]
plant
pod
0.999186
[ "Plant" ]
[ "wn.plant.n.02" ]
raceme
pod
0.999178
[ "Raceme" ]
[ "wn.raceme.n.01", "wn.pod.n.02" ]
light bulb
glass
0.998858
[ "Incandescent light bulb", "Glass" ]
[ "wn.glass.n.01" ]
asparagus
plant
0.998813
[ "Asparagus", "Plant" ]
[ "wn.asparagus.n.01", "wn.plant.n.02" ]
worm
substance
0.99877
[ "Worm" ]
[ "wn.worm.n.01", "wn.substance.n.01" ]
cow
growth hormone
0.998892
[ "Cattle", "Growth hormone" ]
[ "wn.cattle.n.01", "wn.somatotropin.n.01" ]
cucumber
acid
0.999196
[ "Cucumber", "Acid" ]
[ "wn.cucumber.n.02", "wn.acid.n.01" ]
river otter
gland
0.999357
[ "Gland" ]
[ "wn.river_otter.n.01", "wn.gland.n.01" ]
plant
spore
0.999018
[ "Plant", "Spore" ]
[ "wn.plant.n.02", "wn.spore.n.01" ]
eye
layer
0.999074
[ "Eye" ]
[ "wn.eye.n.01", "wn.layer.n.02" ]
owl
body
0.999097
[ "Owl" ]
[ "wn.owl.n.01", "wn.body.n.01" ]
moth
antenna
0.999325
[ "Moth" ]
[ "wn.moth.n.01", "wn.antenna.n.03" ]

Dataset Card for [HasPart]

Dataset Summary

This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old鈥檚 vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet.

Supported Tasks and Leaderboards

Text Classification / Scoring - meronyms (e.g., plant has part stem)

Languages

English

Dataset Structure

Data Instances

[More Information Needed]

{'arg1': 'plant',
 'arg2': 'stem',
 'score': 0.9991798414303377,
 'synset': ['wn.plant.n.02', 'wn.stalk.n.02'],
 'wikipedia_primary_page': ['Plant']}

Data Fields

  • arg1, arg2: These are the entities of the meronym, i.e., arg1 has_part arg2
  • score: Meronymic score per the procedure described below
  • synset: Ontological classification from WordNet for the two entities
  • wikipedia_primary_page: Wikipedia page of the entities

Note: some examples contain synset / wikipedia info for only one of the entities.

Data Splits

Single training file

Dataset Creation

Our approach to hasPart extraction has five steps:

  1. Collect generic sentences from a large corpus
  2. Train and apply a RoBERTa model to identify hasPart relations in those sentences
  3. Normalize the entity names
  4. Aggregate and filter the entries
  5. Link the hasPart arguments to Wikipedia pages and WordNet senses

Rather than extract knowledge from arbitrary text, we extract hasPart relations from generic sentences, e.g., 鈥淒ogs have tails.鈥, in order to bias the process towards extractions that are general (apply to most members of a category) and salient (notable enough to write down). As a source of generic sentences, we use GenericsKB, a large repository of 3.4M standalone generics previously harvested from a Webcrawl of 1.7B sentences.

Annotations

Annotation process

For each sentence S in GenericsKB, we identify all noun chunks in the sentence using a noun chunker (spaCy's Doc.noun chunks). Each chunk is a candidate whole or part. Then, for each possible pair, we use a RoBERTa model to classify whether a hasPart relationship exists between them. The input sentence is presented to RoBERTa as a sequence of wordpiece tokens, with the start and end of the candidate hasPart arguments identified using special tokens, e.g.:

[CLS] [ARG1-B]Some pond snails[ARG1-E] have [ARG2-B]gills[ARG2-E] to breathe in water.

where [ARG1/2-B/E] are special tokens denoting the argument boundaries. The [CLS] token is projected to two class labels (hasPart/notHasPart), and a softmax layer is then applied, resulting in output probabilities for the class labels. We train with cross-entropy loss. We use RoBERTa-large (24 layers), each with a hidden size of 1024, and 16 attention heads, and a total of 355M parameters. We use the pre-trained weights available with the model and further fine-tune the model parameters by training on our labeled data for 15 epochs. To train the model, we use a hand-annotated set of 鈭2k examples.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc{bhakthavatsalam2020dogs, title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations}, author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark}, year={2020}, eprint={2006.07510}, archivePrefix={arXiv}, primaryClass={cs.CL} }

Contributions

Thanks to @jeromeku for adding this dataset.

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