Home

Awesome

AKGR: Awesome Knowledge Graph Reasoning

AKGR is a collection of knowledge graph reasoning works, including papers, codes and datasets :fire:. Any problems, please contact liangke200694@126.com or liangke200694@gmail.com. Any other interesting papers or codes are welcome. If you find this repository useful to your research or work, it is really appreciated to star this repository.:heart:

GitHub stars GitHub forks

<div align="center"> <img src="./logo_new.png" width=100% /> </div>

🌻 If the corresponding survey paper is also useful for you, please cite here:

@ARTICLE{10577554,
  author={Liang, Ke and Meng, Lingyuan and Liu, Meng and Liu, Yue and Tu, Wenxuan and Wang, Siwei and Zhou, Sihang and Liu, Xinwang and Sun, Fuchun and He, Kunlun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal}, 
  year={2024},
  volume={},
  number={},
  pages={1-20},
  doi={10.1109/TPAMI.2024.3417451}}

Bookmarks <span id="bookmarks"></span>

Survey Papers <span id="survey-papers-"></span>

YearTitleVenuePaperCode
2024A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal (Ours)TPAMILinkLink
2023Unifying Large Language Models and Knowledge Graphs: A RoadmaparXivLinkLink
2023A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and ProspectsarXivLinkLink
2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge GraphsarXivLink-
2022Knowledge Graph Reasoning with Logics and Embeddings: Survey and PerspectivearXivLink-
2022An Overview of Knowledge Graph Reasoning: Key Technologies and ApplicationsJournal of Sensor and Actuator NetworksLink-
2021Neural, symbolic and neural-symbolic reasoning on knowledge graphsOpen AILink-
2020Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoningSemantic WebLink-
2020A Review: Knowledge Reasoning over Knowledge GraphESWALink-

Back

Datasets <span id="datasets-"></span>

Static KGR Datasets <span id="static-knowledge-graphs-"></span>

Transductive Datasets <span id="transductive-datasets-"></span>

Dataset# Entities# Relations# Train Triplets# Val. Triplets# Test Triplets# Description
ATOMIC30951596105368770087701Link
Countries271211102424Link
CoDEX-S2034423288836543656Link
CoDEX-M17050511855842062020622Link
CoDEX-L77951695511933062230622Link
ConceptNet28370083362725993334074923407492Link
ConceptNet100K783343410000012001200Link
DBpedia50499006543238839910969Link
DBpedia5005174756543102677100001155937Link
DB100K996044705974824999750000Link
FAMILY3007122348320382835Link
FB1375043133162321181647464Link
FB122973812291638959511243Link
FB15k1495113454831425000059071Link
FB20k1992313454728604899190149Link
FB24k23634673402493-21067Link
FB15K-237145412372721151753520466Link
FB60K-NYT1069514132726828087658918Link
Hetionet47031241800157225020225020Link
Kinship10425854410681074Link
Location44553846565Link
Nation14551592199201Link
NELL23K229252002544549614952Link
NELL-9957549220012617650005000Link
OpenBioLink184765284192002188394183011Link
ogbl-biokg93773514762678162886162780Link
ogbl-wikikg2250060453516109182429456598543Link
OpenBG500249743500124255050005000Link
OpenBG500-L2782223500474100321000010000Link
Sport103941349358358Link
Toy2801124565109152Link
UMLS135465216652661Link
UMLS-PubMed59226443203084187568689Link
WD-singer102821351614221632203Link
WN113869611110361521221035Link
WN18409431814144250005000Link
WN18RR40943118683529242824Link
wikidata5m45944858222061427951635163Link
YAGO3-1012318237107904049784982Link
YAGO37123189374206235000050000Link
M-/YAGO39K854843935499793419364Link

Back

Inductive Datasets <span id="inductive-datasets-"></span>

<table> <thead> <tr> <th colspan="2">Dataset</th> <th># Entities<br></th> <th># Relations</th> <th># Train Triplets<br></th> <th># Val. Triplets<br></th> <th># Test. Triplets<br></th> <th># Description</th> </tr> </thead> <tbody> <tr> <td rowspan="2" align="center"><a href="https://drive.google.com/file/d/17mNlNNYJkL2GejlgtUSeOxWQkuiz7YoJ/view?usp=share_link"> WN18RRv1 </a></td> <td align="center">train-graph</td> <td align="center">2746</td> <td align="center">9</td> <td align="center">5410</td> <td align="center">626</td> <td align="center">638</td> <td align="center" rowspan="24"> <a href="http://proceedings.mlr.press/v119/teru20a/teru20a-supp.pdf"> Link </a></td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">922</td> <td align="center">9</td> <td align="center">1618</td> <td align="center">181</td> <td align="center">184</td> </tr> <tr> <td rowspan="2" align="center"><a href="https://drive.google.com/file/d/1J7CaqaLx3ZEyUA9j9eyvdZoWaws9hzGq/view?usp=share_link"> WN18RRv2 </a></td> <td align="center">train-graph</td> <td align="center">6954</td> <td align="center">10</td> <td align="center">15262</td> <td align="center">1837</td> <td align="center">1868</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">2923</td> <td align="center">10</td> <td align="center">4011</td> <td align="center">407</td> <td align="center">437</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1a2auqD9VJ9_J2EshcZsR6_pjsXXa30HS/view?usp=share_link"> WN18RRv3 </a></td> <td align="center">train-graph</td> <td align="center">12078</td> <td align="center">11</td> <td align="center">25901</td> <td align="center">3097</td> <td align="center">3152</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">5084</td> <td align="center">11</td> <td align="center">6327</td> <td align="center">534</td> <td align="center">601</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/11rjf3vpwIKi5AQkSSXNeBF8yGShrOPa5/view?usp=share_link"> WN18RRv4 </a></td> <td align="center">train-graph</td> <td align="center">3861</td> <td align="center">9</td> <td align="center">7940</td> <td align="center">934</td> <td align="center">968</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">7208</td> <td align="center">9</td> <td align="center">12334</td> <td align="center">1394</td> <td align="center">1429</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1vjul-zXFavPLBku9lLbhajMwm_t0de83/view?usp=share_link"> FB15k237v1 </a></td> <td align="center">train-graph</td> <td align="center">2000</td> <td align="center">183</td> <td align="center">4245</td> <td align="center">485</td> <td align="center">492</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">1500</td> <td align="center">146</td> <td align="center">1993</td> <td align="center">202</td> <td align="center">201</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/15st6MwSe4wgH0pZArXaP_mO5Hj4mmRNs/view?usp=share_link"> FB15k237v2 </a></td> <td align="center">train-graph</td> <td align="center">3000</td> <td align="center">203</td> <td align="center">9739</td> <td align="center">1166</td> <td align="center">1180</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">2000</td> <td align="center">176</td> <td align="center">4145</td> <td align="center">469</td> <td align="center">478</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1N-D8lrj6mmhYtzoh2N4VFRi_vX0Ub7BU/view?usp=share_link"> FB15k237v3 </a></td> <td align="center">train-graph</td> <td align="center">4000</td> <td align="center">218</td> <td align="center">17986</td> <td align="center">2194</td> <td align="center">2214</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">3000</td> <td align="center">187</td> <td align="center">7406</td> <td align="center">866</td> <td align="center">865</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1EXROw3QUncukmYSI-fjpCctuWpxye4s0/view?usp=share_link"> FB15k237v4 </a></td> <td align="center">train-graph</td> <td align="center">5000</td> <td align="center">222</td> <td align="center">27203</td> <td align="center">3352</td> <td align="center">3361</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">3500</td> <td align="center">204</td> <td align="center">11714</td> <td align="center">1416</td> <td align="center">1424</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/16JjvM1FYPo3tmDTpqaZ1fSs0qdoR3ywN/view?usp=share_link"> NELL995v1 </a></td> <td align="center">train-graph</td> <td align="center">10915</td> <td align="center">14</td> <td align="center">4687</td> <td align="center">414</td> <td align="center">435</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">225</td> <td align="center">14</td> <td align="center">833</td> <td align="center">97</td> <td align="center">96</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1ddWh3l2Bx4ppb-BysBiFtg2MGnbFZJ16/view?usp=share_link"> NELL995v2 </a></td> <td align="center">train-graph</td> <td align="center">2564</td> <td align="center">88</td> <td align="center">8219</td> <td align="center">922</td> <td align="center">968</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">4937</td> <td align="center">79</td> <td align="center">4586</td> <td align="center">455</td> <td align="center">476</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/1KGoi1VB5W5vNlIvU0Kmbcr9ha9i7-_9D/view?usp=share_link"> NELL995v3 </a></td> <td align="center">train-graph</td> <td align="center">4647</td> <td align="center">142</td> <td align="center">16393</td> <td align="center">1851</td> <td align="center">1873</td> </tr> <tr> <td align="center">ind-test-graph</td> <td align="center">4921</td> <td align="center">122</td> <td align="center">8048</td> <td align="center">811</td> <td align="center">809</td> </tr> <tr> <td align="center" rowspan="2"><a href="https://drive.google.com/file/d/12cPRA1ICVn0Cd3VgjNg1aQODkOM_TZrT/view?usp=share_link"> NELL995v4 </a></td> <td align="center" >train-graph</td> <td align="center" >2092</td> <td align="center" >77</td> <td align="center" >7546</td> <td align="center" >876</td> <td align="center" >867</td> </tr> <tr> <td align="center" >ind-test-graph</td> <td align="center" >3294</td> <td align="center" >61</td> <td align="center" >7073</td> <td align="center" >716</td> <td align="center" >731</td> </tr> <tr> <td align="center" rowspan="6"><a href="https://drive.google.com/file/d/1W6Y5JIsk72x8Qsw7RGNH2V1pxeHhrwRJ/view?usp=share_link"> WN-MBE </a></td> <td align="center" >train-graph</td> <td align="center" >19361</td> <td align="center" >11</td> <td align="center" >35426</td> <td align="center" >8858</td> <td align="center" >-</td> <td align="center" rowspan="24"><br><br><a href="https://arxiv.org/pdf/2208.10378.pdf"> Link </a></td> </tr> <tr> <td align="center" >ind-test-graph-1</td> <td align="center" >3723</td> <td align="center" >11</td> <td align="center" >5678</td> <td align="center" >-</td> <td align="center" >1352</td> </tr> <tr> <td align="center" >ind-test-graph-2</td> <td align="center" >4122</td> <td align="center" >11</td> <td align="center" >6730</td> <td align="center" >-</td> <td align="center" >1874</td> </tr> <tr> <td align="center" >ind-test-graph-3</td> <td align="center" >4300</td> <td align="center" >11</td> <td align="center" >7545</td> <td align="center" >-</td> <td align="center" >2054</td> </tr> <tr> <td align="center" >ind-test-graph-4</td> <td align="center" >4467</td> <td align="center" >11</td> <td align="center" >8623</td> <td align="center" >-</td> <td align="center" >2493</td> </tr> <tr> <td align="center" >ind-test-graph-5</td> <td align="center" >4514</td> <td align="center" >11</td> <td align="center" >9608</td> <td align="center" >-</td> <td align="center" >2762</td> </tr> <tr> <td align="center" rowspan="6"><a href="https://drive.google.com/file/d/1D36P45Cu3TxpilkMf0vOVAXA7dVC6YLv/view?usp=share_link"> FB-MBE </a></td> <td align="center">train-graph</td> <td align="center">7203</td> <td align="center">237</td> <td align="center">125769</td> <td align="center">31442</td> <td align="center">-</td> </tr> <tr> <td align="center">ind-test-graph-1</td> <td align="center">1458</td> <td align="center">237</td> <td align="center">18394</td> <td align="center">-</td> <td align="center">9240</td> </tr> <tr> <td align="center">ind-test-graph-2</td> <td align="center">1461</td> <td align="center">237</td> <td align="center">19120</td> <td align="center">-</td> <td align="center">9669</td> </tr> <tr> <td align="center">ind-test-graph-3</td> <td align="center">1467</td> <td align="center">237</td> <td align="center">19740</td> <td align="center">-</td> <td align="center">9887</td> </tr> <tr> <td align="center">ind-test-graph-4</td> <td align="center">1467</td> <td align="center">237</td> <td align="center">22455</td> <td align="center">-</td> <td align="center">11127</td> </tr> <tr> <td align="center">ind-test-graph-5</td> <td align="center">1471</td> <td align="center">237</td> <td align="center">22214</td> <td align="center">-</td> <td align="center">11059</td> </tr> <tr> <td align="center" rowspan="6"><a href="https://drive.google.com/file/d/1Z15dbTO6SnOHZIjHq0jo8zJ6USx2B9P3/view?usp=share_link"> NELL-MBE </a></td> <td align="center">train-graph</td> <td align="center">33348</td> <td align="center">200</td> <td align="center">88814</td> <td align="center">22203</td> <td align="center">-</td> </tr> <tr> <td align="center">ind-test-graph-1</td> <td align="center">34488</td> <td align="center">3200</td> <td align="center">34496</td> <td align="center">-</td> <td align="center">3853</td> </tr> <tr> <td align="center">ind-test-graph-2</td> <td align="center">36031</td> <td align="center">3200</td> <td align="center">35411</td> <td align="center">-</td> <td align="center">31059</td> </tr> <tr> <td align="center">ind-test-graph-3</td> <td align="center">37660</td> <td align="center">3200</td> <td align="center">36543</td> <td align="center">-</td> <td align="center">31277</td> </tr> <tr> <td align="center">ind-test-graph-4</td> <td align="center">39056</td> <td align="center">3200</td> <td align="center">37667</td> <td align="center">-</td> <td align="center">31427</td> </tr> <tr> <td align="center">ind-test-graph-5</td> <td align="center">310616</td> <td align="center">3200</td> <td align="center">38876</td> <td align="center">-</td> <td align="center">31595</td> </tr> </tbody> </table>

Back

Temporal KGR Datasets <span id="temporal-knowledge-graphs-"></span>

Dataset# Entities# Relations# Timestamps# Train Triplets# Val. Triplets# Test Triplets# Description
DBpedia-3SP6696796831032113000-Link
GDELT769124089251033270238765305241Link
GDELT-small500203662735685341961341961Link
GDELT-m105020302211322760827926Link
IMDB-13-3SP3244455143791377310000-Link
IMDB-30SP243148143062109630003000Link
ICSES05-151048825140173869624609246275Link
ICEWS11-14673823514611187661485914756Link
ICSES147128230365636851382313222Link
ICEWS14-Plus71282303657282689418963Link
ICEWS182303325672723730184599549545Link
YAGO11k/YOGA10623101891615401952320026Link
YAGO-3SP2700937312475730003000Link
YAGO15k15403341981104411381513800Link
YAGO18301003810205512051097310973Link
WIKI/Wikidata12k12554242322735685341961341961Link
Wikidata11k11134953282428442874814283Link
Wikidata-big125726203170032363550005000Link

Back

Multi-Modal KGR Datasets <span id="multi-modal-knowledge-graphs-"></span>

<table> <thead> <tr> <th colspan="2">Dataset</th> <th># Modality </th> <th># Entities</th> <th># Relations</th> <th># Train Triplets</th> <th>#&nbsp;Val. Triplets</th> <th>#&nbsp;Test. Triplets</th> <th>#&nbsp;Description</th> </tr> </thead> <tbody> <tr> <td align="center" colspan="2" rowspan="3"><a href="https://drive.google.com/file/d/1Gv15F4si9ngJAiNEgw0x3oYgPlt2WfBh/view?usp=share_link">FB-IMG-TXT</a></td> <td align="center" >KG</td> <td align="center" >11757</td> <td align="center" rowspan="3">1231</td> <td align="center" rowspan="3">285850</td> <td align="center" rowspan="3">34863</td> <td align="center" rowspan="3">29580</td> <td align="center" rowspan="3"><a href="https://aclanthology.org/S18-2027.pdf">Link</a></td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >11757</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >1175700</td> </tr> <tr> <td align="center" colspan="2" rowspan="2"><a href="https://drive.google.com/file/d/1ez-uaC-QtkT5LGfZJHVEE0OMWXMXABon/view?usp=sharing">FB15k-237-IMG</a></td> <td align="center" >KG</td> <td align="center" >14541</td> <td align="center" rowspan="2">237</td> <td align="center" rowspan="2">272115</td> <td align="center" rowspan="2">17535</td> <td align="center" rowspan="2">20466</td> <td align="center" rowspan="2"><a href="https://arxiv.org/pdf/2205.02357.pdf">Link</a></td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >145410</td> </tr> <tr> <td align="center" colspan="2" rowspan="2"><a href="https://imgpedia.dcc.uchile.cl/">IMGpedia</a></td> <td align="center" >KG</td> <td align="center" >14765300</td> <td align="center" rowspan="2">442959000</td> <td align="center" rowspan="2">3119207705</td> <td align="center" rowspan="2">-</td> <td align="center" rowspan="2">-</td> <td align="center" rowspan="2"><a href="http://sferrada.com/pdf/imgpediaISWC17.pdf">Link</a></td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >44295900</td> </tr> <tr> <td align="center" rowspan="9"><a href="https://drive.google.com/file/d/1AA7BIpP3VJm2fsbKa4fOxGTtZXjOERm8/view?usp=share_link">MMKG</a></td> <td align="center" rowspan="3">MMKG-FB15k</td> <td align="center" >KG</td> <td align="center" >14951</td> <td align="center" rowspan="3">1345</td> <td align="center" >592213</td> <td align="center" >-</td> <td align="center" >-</td> <td align="center" rowspan="9"><a href="https://arxiv.org/pdf/1903.05485.pdf">Link</a></td> </tr> <tr> <td align="center" >Numeric Literal</td> <td align="center" >29395</td> <td align="center" >29395</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >13444</td> <td align="center" >13444</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" rowspan="3">MMKG-DB15k</td> <td align="center" >KG</td> <td align="center" >14777</td> <td align="center" rowspan="3">279</td> <td align="center" >99028</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" >Numeric Literal</td> <td align="center" >46121</td> <td align="center" >46121</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >12841</td> <td align="center" >12841</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" rowspan="3">MMKG-Yago15k</td> <td align="center" >KG</td> <td align="center" >15283</td> <td align="center" rowspan="3">32</td> <td align="center" >122886</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" >Numeric Literal</td> <td align="center" >48405</td> <td align="center" >48405</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >11194</td> <td align="center" >11194</td> <td align="center" >-</td> <td align="center" >-</td> </tr> <tr> <td align="center" colspan="2" rowspan="3">MKG-Wikipedia</td> <td align="center" >KG</td> <td align="center" >15000</td> <td align="center" rowspan="3">169</td> <td align="center" rowspan="3">34196</td> <td align="center" rowspan="3">4274</td> <td align="center" rowspan="3">4276</td> <td align="center" rowspan="6"><a href="https://dl.acm.org/doi/abs/10.1145/3503161.3548388">Link</a></td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >14123</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >14463</td> </tr> <tr> <td align="center" colspan="2" rowspan="3">MKG-YAGO</td> <td align="center" >KG</td> <td align="center" >15000</td> <td align="center" rowspan="3">28</td> <td align="center" rowspan="3">21310</td> <td align="center" rowspan="3">2663</td> <td align="center" rowspan="3">2665</td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >12305</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >14244</td> </tr> <tr> <td align="center" colspan="2" rowspan="3"><a href="https://drive.google.com/file/d/1jg4YcFgOfgjUJCnxBjw9w-6ID8VS_L-X/view?usp=sharing ">OpenBG-IMG</a></td> <td align="center" >KG</td> <td align="center" >27910</td> <td align="center" rowspan="3">136</td> <td align="center" rowspan="3">230087</td> <td align="center" rowspan="3">5000</td> <td align="center" rowspan="3">14675</td> <td align="center" rowspan="3"><a href="https://arxiv.org/pdf/2209.15214.pdf">Link</a></td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >27910</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >14718</td> </tr> <tr> <tr> <td align="center" colspan="2" rowspan="2"><a href="https://github.com/wangmengsd/richpedia">RichPedia</a></td> <td align="center" >KG</td> <td align="center" >29985</td> <td align="center" rowspan="2">3</td> <td align="center" rowspan="2">119669570</td> <td align="center" rowspan="2">-</td> <td align="center" rowspan="2">-</td> <td align="center" rowspan="2"><a href="https://link.springer.com/chapter/10.1007/978-3-030-41407-8_9">Link</a></td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >2914770</td> </tr> <tr> <td align="center" colspan="2" rowspan="3"><a href="https://drive.google.com/file/d/1NF7i5GWdbii5sD7o_-fWK0KkGQcanmHd/view?usp=share_link">WN9-IMG-TXT</a></td> <td align="center" >KG</td> <td align="center" >6555</td> <td align="center" rowspan="3">9</td> <td align="center" rowspan="3">11741</td> <td align="center" rowspan="3">1319</td> <td align="center" rowspan="3">1337</td> <td align="center" rowspan="3"><a href="https://arxiv.org/pdf/1609.07028.pdf">Link</a></td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >6555</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >63225</td> </tr> <tr> <td align="center" colspan="2" rowspan="2"><a href="https://drive.google.com/file/d/1ez-uaC-QtkT5LGfZJHVEE0OMWXMXABon/view?usp=sharing">WN18-IMG</a></td> <td align="center" >KG</td> <td align="center" >40943</td> <td align="center" rowspan="2">18</td> <td align="center" rowspan="2">141442</td> <td align="center" rowspan="2">5000</td> <td align="center" rowspan="2">5000</td> <td align="center" rowspan="2"><a href="https://arxiv.org/pdf/2205.02357.pdf">Link</a></td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >145410</td> </tr> <tr> <td align="center" colspan="2" rowspan="4"><a href="https://github.com/pouyapez/mkbe/tree/master/datasets/Movielens-100k%20plus">MovieLens-100k plus</a></td> <td align="center" >KG</td> <td align="center" >2625</td> <td align="center" rowspan="4">13</td> <td align="center" rowspan="4">100000</td> <td align="center" rowspan="4">-</td> <td align="center" rowspan="4">-</td> <td align="center" rowspan="8"><a href="https://arxiv.org/pdf/1809.01341">Link</a></td> </tr> <tr> <td align="center" >Numerical</td> <td align="center" >2625</td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >1682</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >1651</td> </tr> <tr> <td align="center" colspan="2" rowspan="4"><a href="https://github.com/pouyapez/mkbe/tree/master/datasets/YAGO-10%20plus">Yago-10 plus</a></td> <td align="center" >KG</td> <td align="center" >123182</td> <td align="center" rowspan="4">45</td> <td align="center" rowspan="4">1079040</td> <td align="center" rowspan="4">-</td> <td align="center" rowspan="4">-</td> </tr> <tr> <td align="center" >Numerical</td> <td align="center" >111406</td> </tr> <tr> <td align="center" >TXT</td> <td align="center" >107326</td> </tr> <tr> <td align="center" >IMG</td> <td align="center" >61246</td> </tr> </tbody> </table>

Back

Static Knowledge Graph Reasoning <span id="static-knowledge-graph-reasoning-"></span>

Translational Models <span id="translational-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024GoldEGeneralizing Knowledge Graph Embedding with Universal Orthogonal ParameterizationICMLTransductiveLinkLink
2023CompoundE3DKnowledge Graph Embedding with 3D Compound Geometric TransformationsarXivTransductiveLink-
2023EXPRESSIVEEXPRESSIVE: A SPATIO-FUNCTIONAL EMBEDDING FOR KNOWLEDGE GRAPH COMPLETIONICLRTransductiveLinkLink
2022DualDEDualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper ReasoningWSDMTransductiveLink-
2022TripleRETripleRE: Knowledge Graph Embeddings via Tripled Relation VectorsarXivTransductiveLink-
2022InterHTInterHT: Knowledge Graph Embeddings by Interaction between Head and Tail EntitiesarXivTransductiveLink-
2022HousEHousE: Knowledge Graph Embedding with Householder ParameterizationICMLTransductiveLinkLink
2022ReflectEKnowledge graph embedding by reflection transformationKBSTransductivelink-
2022DensEDensE: An enhanced non-commutative representation for knowledge graph embedding with adaptive semantic hierarchyNCTransductivelink-
2022StructurEStructural context-based knowledge graph embedding for link predictionNCTransductivelink-
2021HA-RotatEHierarchical-aware relation rotational knowledge graph embedding for link predictionNCTransductivelink-
2021PairREPairRE: Knowledge Graph Embeddings via Paired Relation VectorsACLTransductivelinklink
2021CyclECycle or Minkowski: Which is More Appropriate for Knowledge Graph EmbeddingCIKMTransductivelink-
2021MöbiusEMöbiusE: Knowledge Graph Embedding on Möbius RingKBSTransductivelinklink
20215*E5 Knowledge Graph Embeddings with Projective Transformations*AAAITransductivelink-
2021BiQUEBiQUE: Biquaternionic Embeddings of Knowledge GraphsEMNLPTransductivelinklink
2021HBEHyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link PredictionEMNLPTransductivelink-
2021RotLHyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph EmbeddingsEMNLPTransductivelink-
2021GrpKGKnowledge Graph Representation Learning as Groupoid: Unifying TransE, RotatE, QuatE, ComplExCIKMTransductivelink-
2021MQuadEMQuadE: a Unified Model for Knowledge Fact EmbeddingWWWTransductivelink-
2020ConnectEKnowledge graph entity typing via learning connecting embeddingsKBSTransductivelink-
2020MAKRAn asymmetric knowledge representation learning in manifold spaceISTransductivelink-
2020HAKELearning Hierarchy-Aware Knowledge Graph Embeddings for Link PredictionAAAITransductivelinklink
2020BoxEBoxE: A Box Embedding Model for Knowledge Base CompletionNeurIPSTransductivelink-
2020OTEOrthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph EmbeddingACLTransductivelinklink
2020TransRHSTransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical StructureIJCAITransductivelinklink
2020MDEMDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsECAITransductivelinklink
2020AprilEAprilE: Attention with Pseudo Residual Connection for Knowledge Graph EmbeddingCOLINGTransductivelink-
2020RatERatE: Relation-Adaptive Translating Embedding for Knowledge Graph CompletionCOLINGTransductivelink-
2020Rotate3DRotate3D: Representing Relations as Rotations in Three-Dimensional Space for Knowledge Graph EmbeddingCIKMTransductivelinklink
2020LineaRELineaRE: Simple but Powerful Knowledge Graph Embedding for Link PredictionICDMTransductivelinkLink
2020GeomEKnowledge Graph Embeddings in Geometric AlgebrasCOLINGTransductivelink-
2020SpacEssFantastic Knowledge Graph Embeddings and How to Find the Right Space for ThemISWCTransductivelink-
2020HyperKGHyperbolic Knowledge Graph Embeddings for Knowledge Base CompletionESWCTransductivelinklink
2019RotatERotatE: Knowledge Graph Embedding by Relational Rotation in Complex SpaceICLRTransductivelinklink
2019TransGateTransGate: Knowledge Graph Embedding with Shared Gate StructureAAAITransductivelink-
2019TransMSTransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional SemanticsIJCAITransductivelink-
2019TransWComposing Knowledge Graph Embeddings via Word EmbeddingsarXivInductivelink-
2019MuRPMulti-relational Poincaré Graph EmbeddingsNeurIPSTransductivelinklink
2018TransAtTranslating Embeddings for Knowledge Graph Completion with Relation Attention MechanismIJCAITransductivelinklink
2018TorusETorusE: Knowledge Graph Embedding on a Lie GroupAAAITransductiveLinklink
2018TransCDifferentiating Concepts and Instances for Knowledge Graph EmbeddingEMNLPTransductivelinklink
2017puTransENon-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge GraphsAAAITransductivelink-
2017ITransFAn Interpretable Knowledge Transfer Model for Knowledge Base CompletionACLTransductivelink-
2017CombinERepresentation Learning of Large-Scale Knowledge Graphs via Entity Feature CombinationsCIKMTransductivelink-
2017Trans-RSLearning Knowledge Embeddings by Combining Limit-based Scoring LossCIKMTransductivelink-
2016TransALocally Adaptive Translation for Knowledge Graph EmbeddingAAAITransductivelink-
2016TranSparseKnowledge Graph Completion with Adaptive Sparse Transfer MatrixAAAITransductivelink-
2016TransGTransG: A Generative Model for Knowledge Graph EmbeddingACLTransductivelinklink
2016ManifoldEFrom One Point to a Manifold: Knowledge Graph Embedding for Precise Link PredictionIJCAITransductivelinklink
2016FTKnowledge Graph Embedding by Flexible TranslationKRTransductivelinklink
2016lppTransA Translation-Based Knowledge Graph Embedding Preserving Logical Property of RelationsNAACL-HLTTransductivelinklink
2016STransESTransE: A Novel Embedding Model of Entities and Relationships in Knowledge BasesNAACL-HLTTransductivelinklink
2015TransDKnowledge Graph Embedding via Dynamic Mapping MatrixACLTransductivelink-
2015TransRLearning Entity and Relation Embeddings for Knowledge Graph CompletionAAAITransductivelinklink
2015RTransEComposing Relationships with TranslationsEMNLPTransductivelinklink
2015KG2ELearning to Represent Knowledge Graphs with Gaussian EmbeddingCIKMTransductivelink-
2014TransMTransition-based Knowledge Graph Embedding with Relational Mapping PropertiesPACLICTransductivelink-
2014TransHKnowledge Graph Embedding by Translating on HyperplanesAAAITransductivelink-
2013TransETranslating Embeddings for Modeling Multi-relational DataNeurIPSTransductivelink-

Back

Tensor Decompositional Models <span id="tensor-decompositional-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024NestENestE: Modeling Nested Relational Structures for Knowledge Graph ReasoningAAAITransductiveLinkLink
2024CompilEModeling Knowledge Graphs with Composite ReasoningAAAITransductiveLinkLink
2022QuatREQuatRE: Relation-Aware Quaternions for Knowledge Graph EmbeddingsWWWTransductiveLinkLink
2022GIEGeometry Interaction Knowledge Graph EmbeddingsAAAITransductiveLinkLink
2021HopfEHopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCIKMTransductivelinklink
2021DualEDual Quaternion Knowledge Graph EmbeddingsAAAITransductivelinklink
2020SEEKSEEK: Segmented Embedding of Knowledge GraphsACLTransductiveLinkLink
2020LowFERLowFER: Low-rank Bilinear Pooling for Link PredictionICMLTransductiveLinkLink
2020B-CPA Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph EmbeddingsEMNLPTransductiveLink-
2020BTDEBTDE: Block Term Decomposition Embedding for Link Prediction in Knowledge GraphECAITransductiveLink-
2020MEIMulti-Partition Embedding Interaction with Block Term Format for Knowledge Graph CompletionECAITransductiveLinkLink
2019QuatEQuaternion Knowledge Graph EmbeddingsNeurIPSTransductiveLinkLink
2019DihEdralRelation Embedding with Dihedral Group in Knowledge GraphACLTransductiveLink-
2019TuckERTuckER: Tensor Factorization for Knowledge Graph CompletionEMNLPTransductiveLinkLink
2019CrossEInteraction Embeddings for Prediction and Explanation in Knowledge GraphsWSDMTransductiveLinkLink
2018HOLEXExpanding Holographic Embeddings for Knowledge CompletionNeurIPSTransductiveLink-
2018SimplESimplE Embedding for Link Prediction in Knowledge GraphsNeurIPSTransductiveLinkLink
2017ANALOGYAnalogical Inference for Multi-relational EmbeddingsICMLTransductiveLinkLink
2016HolEHolographic Embeddings of Knowledge GraphsAAAITransductiveLinkLink
2016ComplExComplex Embeddings for Simple Link PredictionICMLTransductiveLinkLink
2015DISTMULTEmbedding Entities and Relations for Learning and Inference in Knowledge BasesICLRTransductiveLink-
2011RESCALA Three-Way Model for Collective Learning on Multi-Relational DataICMLTransductiveLinkLink

Back

Neural Network Models <span id="neural-network-models-"></span>

Tranditional NN Models <span id="traditonal-neural-network-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024LAFALAFA: Multimodal Knowledge Graph Completion with Link Aware Fusion and AggregationAAAITransductiveLinkLink
2017ProjEProjE: Embedding Projection for Knowledge Graph CompletionAAAITransductiveLinkLink
2016NAMProbabilistic Reasoning via Deep Learning: Neural Association ModelsarXivTransductiveLink-
2013SMEA semantic matching energy function for learning with multi-relational dataMachine LearningTransductiveLink-
2013NTNReasoning With Neural Tensor Networks for Knowledge Base CompletionNeurIPSTransductiveLink-

Back

CNN Models <span id="convolutional-neural-network-models-"></span>

YearModel NameTitleVenueScenarioPaperCode
2023ConvRotKnowledge graph embedding by relational rotation and complex convolution for link predictionExpert Syst ApplTransductiveLinkLink
2021ConEConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge GraphsNeurIPSTransductiveLink-
2021ConExConvolutional Complex Knowledge Graph EmbeddingsESWCTransductiveLinkLink
2020InteractEInteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature InteractionsAAAITransductiveLinkLink
2019ConvRAdaptive Convolution for Multi-Relational LearningNAACL-HLTTransductiveLink-
2019HypERHypernetwork Knowledge Graph EmbeddingsICANNTransductiveLink-
2018ConvKBA Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural NetworkNAACL-HLTTransductiveLinkLink
2018ConvEConvolutional 2D Knowledge Graph EmbeddingsAAAITransductiveLinkLink

Back

GNN Models <span id="graph-neural-network-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024HyRelRHKH: Relational Hypergraph Neural Network for Link Prediction on N-ary Knowledge HypergraphACM MMTransductiveLinkLink
2024HyRelGeneralize to Fully Unseen Graphs: Learn Transferable Hyper-Relation Structures for Inductive Link PredictionACM MMTransductiveLinkLink
2024MGTCAMixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph CompletionAAAITransductiveLink-
2024MINESMINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced SubgraphsAAAIInductiveLink-
2023C-MPNNA Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge GraphsNeurIPSInductiveLinkLink
2023AdaPropAdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph ReasoningKDDInductiveLinkLink
2023InGramInGram: Inductive Knowledge Graph Embedding via Relation GraphsICMLInductiveLinkLink
2023REPORTInductive Relation Prediction from Relational Paths and Context with Hierarchical TransformerarXivInductiveLink-
2023DEKG-ILPDisconnected Emerging Knowledge Graph Oriented Inductive Link PredictionICDEInductiveLinkLink
2023RMPIRelational Message Passing for Fully Inductive Knowledge Graph CompletionICDEInductiveLinkLink
2023TransEQTWO BIRDS, ONE STONE: AN EQUIVALENT TRANSFORMATION FOR HYPER-RELATIONAL KNOWLEDGE GRAPH MODELINGarXivTransductiveLink-
2022LogCoInductive Relation Prediction with Logical Reasoning Using Contrastive RepresentationsEMNLPInductiveLinkLink
2022NoGENode Co-occurrence based Graph Neural Networks for Knowledge Graph Link PredictionWSDMTransductiveLink-
2022SGISubgraph Representation Learning with Hard Negative Samples for Inductive Link PredictionICASSPInductiveLink-
2022RED-GNNKnowledge Graph Reasoning with Relational DigraphWWWInductiveLinkLink
2022ConGLRIncorporating Context Graph with Logical Reasoning for Inductive Relation PredictionSIGIRInductiveLink-
2022CFAGExploring Relational Semantics for Inductive Knowledge Graph CompletionAAAIInductiveLink-
2022IKGEOpen-world knowledge graph completion for unseen entities and relations via attentive feature aggregationISInductiveLink-
2022MorsEMeta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingSIGIRInductiveLinkLink
2022BERTRLInductive Relation Prediction by BERTAAAIInductiveLinkLink
2022CBGNNCYCLE REPRESENTATION LEARNING FOR INDUCTIVE RELATION PREDICTIONICLR WorshopInductiveLink-
2022SNRISubgraph Neighboring Relations Infomax for Inductive Link PredictionIJCAIInductiveLinkLink
2022TEMPType-aware Embeddings for Multi-Hop Reasoning over Knowledge GraphsarXivInductiveLinkLink
2022Meta-iKGSubgraph-aware Few-Shot Inductive Link Prediction via Meta-LearningTKDEInductiveLink-
2022CSRFew-shot Relational Reasoning via Connection Subgraph PretrainingNeurIPSInductiveLinkLink
2021KE-GCNKnowledge Embedding Based Graph Convolutional NetworkWWWTransductiveLinkLink
2021DisenKGATDisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention NetworkCIKMTransductiveLinkLink
2021M2GNNMixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph CompletionWWWTransductiveLink-
2021HRFNMeta-Learning Based Hyper-Relation Feature Modeling for Out-of-Knowledge-Base EmbeddingCIKMInductiveLink-
2021GENLearning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionNeurIPSInductiveLinkLink
2021SRGCNSRGCN: Graph-based multi-hop reasoning on knowledge graphsNCTransductiveLink-
2021TRARTarget relational attention-oriented knowledge graph reasoningNCTransductiveLink-
2021KompaReKompaRe: A Knowledge Graph Comparative Reasoning SystemKDDTransductiveLink-
2021INDIGOINDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise EncodingNeurIPSInductiveLink-
2021NBF-NetNeural Bellman-Ford Networks: A General Graph Neural Network Framework for Link PredictionNeurIPSInductiveLinkLink
2021CoMPILECommunicative Message Passing for Inductive Relation ReasoningAAAIInductiveLinkLink
2021TACTTopology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge GraphsAAAIInductiveLinkLink
2021RPC-IRLearning First-Order Rules with Relational Path Contrast for Inductive Relation ReasoningTNNLSInductiveLink-
2020DPMPNDynamically Pruned Message Passing Networks for Large-scale Knowledge Graph ReasoningICLRTransductiveLinkLink
2020RGHATRelational Graph Neural Network with Hierarchical Attention for Knowledge Graph CompletionAAAITransductiveLink-
2020GAEATGAEAT: Graph Auto-Encoder Attention Networks for Knowledge Graph CompletioncikmTransductiveLink-
2020COMPGCNComposition-based Multi-Relational Graph Convolutional NetworksICLRTransductiveLinkLink
2020GraILInductive Relation Prediction by Subgraph ReasoningICMLInductiveLinkLink
2019M-GNNRobust Embedding with Multi-Level Structures for Link PredictionIJCAITransductiveLink-
2019SACNEnd-to-End Structure-Aware Convolutional Networks for Knowledge Base CompletionAAAITransductiveLinkLink
2019A2NA2N: Attending to Neighbors for Knowledge Graph InferenceACLTransductiveLink-
2019KBGATLearning Attention-based Embeddings for Relation Prediction in Knowledge GraphsACLTransductiveLinkLink
2019TransGCNTransGCN: Coupling Transformation Assumptions with Graph Convolutional Networks for Link PredictionK-CAPTransductiveLink-
2019LANLogic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph EmbeddingAAAIInductiveLinkLink
2018RGCNModeling Relational Data with Graph Convolutional NetworksESWCTransductiveLinkLink
2017-Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network ApproachIJCAIInductiveLinkLink

Back

Transformer Models <span id="transformer-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024KnowFormerKnowFormer: Revisiting Transformers for Knowledge Graph ReasoningICMLInductiveLink-
2023iHTPre-training Transformers for Knowledge Graph CompletionarXivInductiveLink-
2023LambdaKGLambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsarXivTransductiveLinkLink
2022RuleformerRuleformer: Context-aware Rule Mining over Knowledge GraphCOLINGTransductiveLinkLink
2022kgTransformerMask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical QueriesKDDInductiveLinkLink
2022KformerKformer: Knowledge Injection in Transformer Feed-Forward LayersNLPCCTransductiveLinkLink
2022TETTransformer-based Entity Typing in Knowledge GraphsEMNLPLinkLink
2022KPGTKPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCOLINGTransductiveLink-
2021HittERHittER: Hierarchical Transformers for Knowledge Graph EmbeddingsEMNLPLinkLink
2020-Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language ModelsCOLINGTransductiveLink-
2020CoKECoKE: Contextualized Knowledge Graph EmbeddingarXivTransductiveLinkLink
2020KG-BERTKG-BERT: BERT for knowledge graph completionAAAITransductiveLinkLink
2020CODEXCoDEx: A Comprehensive Knowledge Graph Completion BenchmarkEMNLPLinkLink
2019ATOMICATOMIC: An Atlas of Machine Commonsense for If-Then ReasoningAAAILinkLink
2023KG-R3A Retrieve-and-Read Framework for Knowledge Graph Link PredictionCIKMTransductiveLinkLink

Back

Path-based Models <span id="path-based-models-"></span>

YearModelTitleVenueScenarioPaperCode
2023AstarNetAstarNet: A Scalable Path-based Reasoning Approach for Knowledge GraphsNeurIPSTransductiveLinkLink
2022CURLLearning to Walk with Dual Agents for Knowledge Graph ReasoningAAAITransductiveLinkLink
2019CPLCollaborative policy learning for open knowledge graph reasoningEMNLPTransductiveLinkLink
2018DIVAVariational Knowledge Graph ReasoningNAACLTransductiveLink-
2018MINERVAGo for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement LearningICLRTransductiveLinkLink
2018Multi-HopMulti-Hop Knowledge Graph Reasoning with Reward ShapingEMNLPTransductiveLinkLink
2018M-WalkM-Walk: Learning to walk over graphs using monte carlo tree searchNeurIPSTransductiveLinkLink
2017LogSumExpChains of Reasoning over Entities, Relations, and Text using Recurrent Neural NetworksEACLTransductiveLinkLink
2017DeepPathDeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningEMNLPTransductiveLinkLink
2015RNNPRACompositional Vector Space Models for Knowledge Base CompletionAAAITransductiveLink-
2014ProPPRIncorporating Vector Space Similarity in Random Walk Inference over Knowledge BasesEMNLPTransductiveLink-
2010PRARelational retrieval using a combination of path-constrained random walksMachine LearningTransductiveLink-

Back

Rule-based Models <span id="rule-based-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024FITRETHINKING COMPLEX QUERIES ON KNOWLEDGE GRAPHS WITH NEURAL LINK PREDICTORSICLRTransductiveLinkLink
2023DiffLogicDifferentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge GraphsNeurIPSInductiveLinkLink
2023NCRLNEURAL COMPOSITIONAL RULE LEARNING FOR KNOWLEDGE GRAPH REASONINGICLRInductiveLinkLink
2023RulENeural-Symbolic Knowledge Graph Reasoning with Rule EmbeddingarXivInductiveLinkLink
2023LSTKLearning from Both Structural and Textual Knowledge for Inductive Knowledge Graph CompletionNeurIPSInductiveLinkLink
2022RFDynamic knowledge graph reasoning based on deep reinforcement learningKnowledge-Based SystemsTransductiveLinkLink
2022RLogicRLogic: Recursive Logical Rule Learning from Knowledge GraphsKDDInductiveLink-
2021RNNLOGICRNNLOGIC: LEARNING LOGIC RULES FOR REASONING ON KNOWLEDGE GRAPHSICLRInductiveLinkLink
2020Neural-Num-LPDifferentiable learning of numerical rules in knowledge graphsICLRInductiveLink-
2020ExpressGNNEfficient probabilistic logic reasoning with graph neural networksICLRInductiveLinkLink
2019IterEIteratively learning embeddings and rules for knowledge graph reasoning,WWWInductiveLinkLink
2019pLogicNetProbabilistic logic neural networks for reasoningNeurIPSInductiveLinkLink
2019DRUMDRUM: End-To-End Differentiable Rule Mining On Knowledge GraphsNeurIPSInductiveLinkLink
2019RLvLRAn Embedding-based Approach to Rule Learning in Knowledge GraphsTKDEInductiveLink-
2018RuleNFine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph CompletionISWCInductiveLink-
2018RUGEKnowledge graph embedding with iterative guidance from soft rulesAAAIInductiveLinkLink
2017NTPEnd-to-end differentiable provingNeurIPSInductiveLink-
2017NeuralLPDifferentiable learning of logical rules for knowledge base reasoningNeurIPSInductiveLinkLink
2016KALEJointly embedding knowledge graphs and logical rulesEMNLPInductiveLinkLink
2013AMIEAMIE: association rule mining under incomplete evidence in ontological knowledge basesWWWInductiveLinkLink

Back

Temporal Knowledge Graph Reasoning <span id="temporal-knowledge-graph-reasoning-"></span>

Evaluation Papers <span id="rnn-based-models-"></span>

YearTitleVenueScenarioPaperCode
2022On the Evaluation of Methods for Temporal Knowledge Graph ForecastingNeurIPSExtrapolationLinkLink

RNN-based Models <span id="rnn-based-models-"></span>

Quadruple-based Models <span id="quadruple-based-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024ECEformerTransformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge GraphSIGIRExtrapolationLinkLink
2024TEILPTEILP: Time Prediction over Knowledge Graphs via Logical ReasoningAAAIInterpolationLinkLink
2023TILPTILP: DIFFERENTIABLE LEARNING OF TEMPORAL LOGICAL RULES ON KNOWLEDGE GRAPHSICLRInterpolationLinkLink
2022EvoKGEvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge GraphsWSDMExtrapolationLinkLink
2021TpmodTPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph CompletionTKDDInterpolationLinkLink
2021HIPHIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge GraphIJCAIExtrapolationLinkLink
2020TeMPTeMP: Temporal Message Passing for Temporal Knowledge Graph CompletionEMNLPInterpolationLinkLink
2018TTransELearning Sequence Encoders for Temporal Knowledge Graph CompletionEMNLPInterpolationLinkLink
2018TA-DISTMULTLearning Sequence Encoders for Temporal Knowledge Graph CompletionEMNLPInterpolationLinkLink
2018TA-TransELearning Sequence Encoders for Temporal Knowledge Graph CompletionEMNLPInterpolationLinkLink
2017Know-EvolveKnow-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsICMLExtrapolationLinkLink

Back

Path-based Models <span id="path-based-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024TILRTemporal inductive logic reasoningIJCAIInterpolationLinkLink
2022ExKGRExKGR: Explainable Multi-hop Reasoning for Evolving Knowledge GraphDASFAAInterpolationLinkLink
2021TimeTravelerTimeTraveler: Reinforcement Learning for Temporal Knowledge Graph ForecastingEMNLPExtrapolationLinkLink
2021CluSTeRSearch from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge GraphsACLExtrapolationLinkLink
2021TpathMulti-hop reasoning over paths in temporal knowledge graphs using reinforcement learningApplied Soft ComputingInterpolationLinkLink
2020DacKGRDynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge GraphEMNLPInterpolationLinkLink

Back

Graph-based Models <span id="graph-based-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024IncDETowards Continual Knowledge Graph Embedding via Incremental DistillationAAAI-LinkLink
2023HGLSLearning Long- and Short-term Representations for Temporal Knowledge Graph ReasoningWWWExtrapolationLinkLink
2023LOGAE-TKGLogistics Audience Expansion via Temporal Knowledge GraphCIKMInterpolationLinkLink
2023LogCLLocal-Global History-aware Contrastive Learning for Temporal Knowledge Graph ReasoningICDEInterpolationLinkLink
2023RETIARETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph ExtrapolationICDEExtrapolationLinkLink
2023RPCLearn from Relational Correlations and Periodic Events for Temporal Knowledge Graph ReasoningSIGIRExtrapolationLinkLink
2023HiSMatchHiSMatch: Historical Structure Matching based Temporal Knowledge Graph ReasoningarXivExtrapolationLinkLink
2022TiRGNTiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph ReasoningIJCAIExtrapolationLinkLink
2022TRHyTETRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational HyperplanesDASFAAInterpolationLinkLink
2021RE-GCNTemporal Knowledge Graph Reasoning Based on Evolutional Representation LearningSIGIRExtrapolationLinkLink
2020EvolveGCNEvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsAAAIExtrapolationLinkLink
2020RE-NETRecurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge GraphsEMNLPExtrapolationLinkLink

Back

RNN-agnostic Models <span id="rnn-agnostic-models-"></span>

Time-Vector Guided Models <span id="time-vector-guided-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024IMEIME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph CompletionWWWExtrapolationLink-
2023DREAMDREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph ReasoningSIGIRExtrapolationLink-
2022TLT-KGEAlong the Time: Timeline-traced Embedding for Temporal Knowledge Graph CompletionCIKMInterpolationLinkLink
2022TempoQRTempoQR: Temporal Question Reasoning over Knowledge GraphsAAAIInterpolationLinkLink
2022BoxTETemporal Knowledge Graph Completion Using Box EmbeddingsAAAIInterpolationLinkLink
2022TuckERTNTTucker decomposition-based temporal knowledge graph completionKBSInterpolationLink-
2022RotateQVSRotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph CompletionACLInterpolationLink-
2021ChronoRChronoR: Rotation Based Temporal Knowledge Graph EmbeddingAAAIInterpolationLink-
2021DBKGELearning Dynamic Embeddings for Temporal Knowledge GraphsWSDMInterpolationLink-
2021CyGNetLearning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation NetworksAAAIExtrapolationLinkLink
2021T-GAPLearning to Walk across Time for Interpretable Temporal Knowledge Graph CompletionSIGIRInterpolationLinkLink
2021-Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified FrameworkEMNLPInterpolationLink-
2021xERTEExplainable Subgraph Reasoning for Forecasting on Temporal Knowledge GraphsICLRExtrapolationLinkLink
2020-Towards Temporal Knowledge Graph Embeddings with Arbitrary Time PrecisionCIKMInterpolationLinkLink
2020TNTComplExTensor Decompositions for Temporal Knowledge Base CompletionICLRInterpolationLinkLink
2018-Deriving Validity Time in Knowledge GraphWWWInterpolationLink-

Back

Time-Operation Guided Models <span id="time-operation-guided-models-"></span>

YearModelTitleVenueScenarioPaperCode
2024HGEHGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric SubspacesAAAIInterpolationLinkLink
2024IncDETowards Continual Knowledge Graph Embedding via Incremental DistillationAAAIExtrapolationLink-
2024TEILPTEILP: Time Prediction over Knowledge Graphs via Logical ReasoningAAAIInterpolationLinkLink
2023CENETTemporal Knowledge Graph Reasoning with Historical Contrastive LearningsAAAIExtrapolationLink-
2022GHTGraph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge GraphsACLExtrapolationLink-
2022DA-NetDA-Net: Distributed Attention Network for Temporal Knowledge Graph ReasoningsCIKMExtrapolationLink-
2022MetaTKGRLearning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge GraphsarXivExtrapolationLink-
2022rGalTModeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder StructureIJCAIExtrapolationLink-
2022FILTFew-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware InformationAKBCInterpolationLink-
2022TKGC-AGPTemporal Knowledge Graph Completion with Approximated Gaussian Process EmbeddingCOLINGInterpolationLink-
2022TlogicTLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal KnowledgeAAAIExtrapolationLinkLink
2022DKGEEfficiently embedding dynamic knowledge graphsKBSInterpolationLinkLink
2022CENComplex Evolutional Pattern Learning for Temporal Knowledge Graph ReasoningACLExtrapolationLinkLink
2021TPRecTime-aware Path Reasoning on Knowledge Graph for RecommendationsTOISExtrapolationLink-
2021-Spatial-Temporal Attention Network for Temporal Knowledge Graph CompletionDASFAAInterpolationLink-
2021TIETIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph CompletionSIGIRInterpolationLinkLink
2021TeLMTemporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector EmbeddingsNAACLInterpolationLinkLink
2021RTFERTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph CompletionNAACLInterpolationLink-
2021TANGOLearning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge GraphsEMNLPExtrapolationLinkLink
2020DyERNIEDyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph CompletionEMNLPInterpolationLink-
2020-Explainable Link Prediction for Emerging Entities in Knowledge GraphsISWCInterpolationLinkLink
2020TDGNNContinuous-Time Link Prediction via Temporal Dependent Graph Neural NetworkWWWExtrapolationLinkLink
2020-Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link PredictionEMNLPExtrapolationLink-
2020ATiSETemporal Knowledge Graph Completion Based on Time Series Gaussian EmbeddingISWCInterpolationLinkLink
2020Diachronic embeddingsDiachronic Embedding for Temporal Knowledge Graph CompletionAAAIInterpolationLinkLink
2020TeRoTeRo: A Time-aware Knowledge Graph Embedding via Temporal RotationCOLINGInterpolationLinkLink
2019DyRepDyRep: Learning Representations over Dynamic GraphsICLRExtrapolationLink-
2018HyTEHyTE: Hyperplane-based Temporally aware Knowledge Graph EmbeddingEMNLPInterpolationLink-
2018ChronoTranslateTemporal reasoning over event knowledge graphsKBCOMInterpolationLink-

Back

Multi-Modal Knowledge Graph Reasoning <span id="multi-modal-knowledge-graph-reasoning-"></span>

Transformer-agnostic Model <span id="transformer-agnostic-models-"></span>

YearModelTitleVenuePaperCode
2023--Multimodal Biological Knowledge Graph Completion via Triple Co-attention MechanismICDELink-
2022MMKGRMMKGR: Multi-hop Multi-modal Knowledge Graph ReasoningarXivLink-
2022MMKRLMMKRL: A robust embedding approach for multi-modal knowledge graph representation learningApplied IntelligenceLink-
2022MKGRL-MSMulti-modal knowledge graphs representation learning via multi-headed self-attentionInformation FusionLink-
2022ZSMMKGImproving Zero-Shot Phrase Grounding via Reasoning on External Knowledge and Spatial RelationsAAAILink-
2022OTKGEOTKGE: Multi-modal Knowledge Graph Embeddings via Optimal TransportNeurIPSLinkLink
2022MM-RNSRelation-enhanced Negative Sampling for Multimodal Knowledge Graph CompletionMMLink-
2022CKGCCross-modal Knowledge Graph Contrastive Learning for Machine Learning Method RecommendationMMLink-
2022MoSEMoSE: Modality Split and Ensemble for Multimodal Knowledge Graph CompletionEMNLPLinkLink
2022MCLEAMulti-modal Contrastive Representation Learning for Entity AlignmentCOLINGLinkLink
2021-Explicit Knowledge Incorporation for Visual ReasoningCVPRLink-
2021EVAVisual Pivoting for (Unsupervised) Entity AlignmentAAAILinkLink
2021HMEAMulti-modal Entity Alignment in Hyperbolic SpacearXivLink-
2021RSMEIs Visual Context Really Helpful for Knowledge Graph? A Representation Learning PerspectiveMMLinkLink
2020MKGATMulti-modal Knowledge Graphs for Recommender SystemsCIKMLink-
2020MuckoMucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question AnsweringIJCAILinkLink
2020GRUCCross-modal Knowledge Reasoning for Knowledge-based Visual Question AnsweringPRLink-
2020-Do Dogs have Whiskers? A New Knowledge Base of hasPart RelationsarXivLink-
2020MMEAMMEA: Entity Alignment for Multi-modal Knowledge GraphICKSEMLink-
2020MRCGNEnd-to-End Entity Classification on Multimodal Knowledge GraphsarXivLinkLink
2019GQAGQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCVPRLinkLink
2019VSUAAligning Linguistic Words and Visual Semantic Units for Image CaptioningMMLinkLink
2019KVQAKVQA: Knowledge-Aware Visual Question AnsweringAAAILinkLink
2019-Answering Visual-Relational Queries in Web-Extracted Knowledge GraphsAKBCLinkLink
2019-MMKG: Multi-Modal Knowledge GraphsESWCLinkLink
2019MKRLKnowledge representation learning with entity descriptions, hierarchical types, and textual relationsInformation Processing & ManagementLink-
2019TransAEMultimodal Data Enhanced Representation Learning for Knowledge GraphsACLLinkLink
2018MKBEEmbedding Multimodal Relational Data for Knowledge Base CompletionACLLinkLink
2018MTRLA multimodal translation-based approach for knowledge graph representation learningSEMLinkLink
2018KR-AMDRepresentation learning of knowledge graphs with entity attributes and multimedia descriptionsBigMMLink-
2017KB-VQAExplicit Knowledge-based Reasoning for Visual Question AnsweringIJCAILink-
2017KBLRNKBLRN: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical FeaturesAUAILink-
2016IKRLImage-embodied Knowledge Representation LearningIJCAILinkLink
2016DKRLRepresentation Learning of Knowledge Graphs with Entity DescriptionsAAAILinkLink
2016CKECollaborative Knowledge Base Embedding for Recommender SystemsSIGKDDLink-

Back

Transformer-based Model <span id="transformer-based-models-"></span>

YearModelTitleVenuePaperCode
2023AIRAspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware EntitiesCIKMLinkLink
2023SGMPTStructure Guided Multi-modal Pre-trained Transformer for Knowledge Graph ReasoningArXivLinkLink
2023IMFIMF: Interactive Multimodal Fusion Model for Link PredictionWWWLinkLink
2022DRAGONDeep Bidirectional Language-Knowledge Graph PretrainingNeurIPSLinkLink
2022MuKEAMuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCVPRLinkLink
2022MSNEAMulti-modal Siamese Network for Entity AlignmentKDDLinkLink
2022HRGATHyper-node Relational Graph Attention Network for Multi-modal Knowledge Graph CompletionACM Transactions on Multimedia Computing, Communications, and ApplicationsLinkLink
2022KPGTKPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionKDDLinkLink
2022MKGformerHybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph CompletionSIGIRLinkLink
2022MarTMultimodal Analogical Reasoning over Knowledge GraphsICLRLinkLink
2022Knowledge-CLIPContrastive Language-Image Pre-Training with Knowledge GraphsNeurIPSLink-
2022TMEGModeling Temporal-Modal Entity Graph for Procedural Multimodal Machine ComprehensionACLLink-
2022VBKGCKnowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative SamplingKDDLink-
2021KrispKrisp: Integrating implicit and symbolic knowledge for open domain knowledge-based vqaCVPRLink-

Back

Useful Libararies <span id="useful-libararies-"></span>

Back