lujiaying / Open-World-TaxoKG-CoLearning

Data and code of AKBC'22 paper "Open-World Taxonomy and Knowledge Graph Co-Learning".
https://www.akbc.ws/2022/papers/11_open_world_taxonomy_and_knowle
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Dataset #11

Closed lujiaying closed 3 years ago

lujiaying commented 3 years ago

~- [ ] DBLP~ ~- [ ] PubMed~ ~- [ ] Wiki-subset~

lujiaying commented 3 years ago

DBLP

lujiaying commented 3 years ago

What relations can be generalized?

<Monkey, eat, banana>

lujiaying commented 3 years ago

Setting on May 17

Two datasets

lujiaying commented 3 years ago

Dataset Stat

Dataset #nodes #edges #taxo_edges #non-taxo_edges setting
WN18RR 40K, 5K, 5K 86K, 3K, 3K 3.7K, 1.2K, 1.3K 4.9K, 1.7K, 1.7K close-world, 96.1% test ent in train
CN-100K 78K, 1.8K, 1.3K 100K, 1.2K, 1.2K 15K, 197, 211 85K, 1003, 989 close-world, 92.8% test ent in train

Preliminary Results

WN18RR MRR H@1 H@3 H@10 CN100K MRR H@1 H@3 H@10
TransE .226 .501
DistMult .43 .39 .44 .49 .09 .05 .10 . 17
ConvE .43 .40 .44 .52 .21 .14 .23 .34
ComplEx .44 .41 .46 .51 .11 .07 .12 .19
RotateE .476 .428 .492 .571
HAKE .497 .452 .516 .582
Most-Freq .026 .015 .025 .045 .073 .035 .060 .144
Hard-Taxo .072 .033 .060 .147
CBR .43 .39 .46 .51
Prob-CBR .48 .43 .49 .57
Taxo-CBR

WN18RR from HAKE CN-100K from Commonsense Knowledge Base Completion with Structural and Semantic Context

lujiaying commented 3 years ago

Heuristic Baselines

Commonsense Knowledge Base Completion Similar Fact Count, Argument Similarity, Max Similarity: these train binary classification models.

Most-Frequent: <h, r, ?> then ? is the most freq tail ent for r.

Hard-Taxonomy

lujiaying commented 3 years ago

Case Based Reasoning A Simple Approach to Case-Based Reasoning in Knowledge Bases, https://openreview.net/forum?id=AEY9tRqlU7

Replace the k-nearest search into taxonomy-guided similar entity search.

Case study

WN18RR

to predict: 06845599 (__trade_name) _member_of_domain_usage 03754979(__metharbital)

similar ents: {'06333653' (name): 1, '06338908' (designation): 1, '06336537' (street_name): 1, '06338278' (pseudonym): 1, '06334512' (fictitious_name): 1, '06343520' (title): 1, '06337307' (__given_name): 1, '06335532' (__filename): 1}

retrieved fact: {('06336537'(__street_name), '_member_of_domain_usage', '02837416' (__thunder, street names for heroin)): 1.0})

CN100K

triple to predict: dog CapableOf bark ranked cor_hs: [('dog', 'CapableOf', 'live'), ('dog', 'CapableOf', 'have sex'), ('dog', 'CapableOf', 'lie down'), ('dog', 'CapableOf', 'sleep'), ('dog', 'CapableOf', 'hear'), ('dog', 'CapableOf', 'relaxation'), ('dog', 'CapableOf', 'have fun'), ('dog', 'CapableOf', 'move'), ('dog', 'CapableOf', 'relax'), ('dog', 'CapableOf', 'listen to music'), ('dog', 'CapableOf', 'shelter'), ('dog', 'CapableOf', 'object'), ('dog', 'CapableOf', 'build'), ('dog', 'CapableOf', 'poop'), ('dog', 'CapableOf', 'live in')] MRR=0.002 hits@1,3,10 =0.000, 0.000, 0.000 triple to predict: pen UsedFor write ranked cor_hs: [('pen', 'UsedFor', 'learn'), ('pen', 'UsedFor', 'live in'), ('pen', 'UsedFor', 'education'), ('pen', 'UsedFor', 'learn thing'), ('pen', 'UsedFor', 'study'), ('pen', 'UsedFor', 'shelter'), ('pen', 'UsedFor', 'store thing'), ('pen', 'UsedFor', 'teach student'), ('pen', 'UsedFor', 'teach'), ('pen', 'UsedFor', 'listen to teacher'), ('pen', 'UsedFor', 'store object'), ('pen', 'UsedFor', 'child'), ('pen', 'UsedFor', 'play game'), ('pen', 'UsedFor', 'teach child'), ('pen', 'UsedFor', 'make friend')] MRR=0.011 hits@1,3,10 =0.000, 0.000, 0.000