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:
<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
- Datasets :fire:
- Static Knowledge Graph Reasoning
- Temporal Knowledge Graph Reasoning
- Multi-Modal Knowledge Graph Reasoning
- Useful Libararies
Survey Papers <span id="survey-papers-"></span>
Year | Title | Venue | Paper | Code |
---|---|---|---|---|
2024 | A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal (Ours) | TPAMI | Link | Link |
2023 | Unifying Large Language Models and Knowledge Graphs: A Roadmap | arXiv | Link | Link |
2023 | A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects | arXiv | Link | Link |
2023 | Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs | arXiv | Link | - |
2022 | Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective | arXiv | Link | - |
2022 | An Overview of Knowledge Graph Reasoning: Key Technologies and Applications | Journal of Sensor and Actuator Networks | Link | - |
2021 | Neural, symbolic and neural-symbolic reasoning on knowledge graphs | Open AI | Link | - |
2020 | Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning | Semantic Web | Link | - |
2020 | A Review: Knowledge Reasoning over Knowledge Graph | ESWA | Link | - |
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 |
---|---|---|---|---|---|---|
ATOMIC | 309515 | 9 | 610536 | 87700 | 87701 | Link |
Countries | 271 | 2 | 1110 | 24 | 24 | Link |
CoDEX-S | 2034 | 42 | 32888 | 3654 | 3656 | Link |
CoDEX-M | 17050 | 51 | 185584 | 20620 | 20622 | Link |
CoDEX-L | 77951 | 69 | 551193 | 30622 | 30622 | Link |
ConceptNet | 28370083 | 36 | 27259933 | 3407492 | 3407492 | Link |
ConceptNet100K | 78334 | 34 | 100000 | 1200 | 1200 | Link |
DBpedia50 | 49900 | 654 | 32388 | 399 | 10969 | Link |
DBpedia500 | 517475 | 654 | 3102677 | 10000 | 1155937 | Link |
DB100K | 99604 | 470 | 597482 | 49997 | 50000 | Link |
FAMILY | 3007 | 12 | 23483 | 2038 | 2835 | Link |
FB13 | 75043 | 13 | 316232 | 11816 | 47464 | Link |
FB122 | 9738 | 122 | 91638 | 9595 | 11243 | Link |
FB15k | 14951 | 1345 | 483142 | 50000 | 59071 | Link |
FB20k | 19923 | 1345 | 472860 | 48991 | 90149 | Link |
FB24k | 23634 | 673 | 402493 | - | 21067 | Link |
FB15K-237 | 14541 | 237 | 272115 | 17535 | 20466 | Link |
FB60K-NYT10 | 69514 | 1327 | 268280 | 8765 | 8918 | Link |
Hetionet | 47031 | 24 | 1800157 | 225020 | 225020 | Link |
Kinship | 104 | 25 | 8544 | 1068 | 1074 | Link |
Location | 445 | 5 | 384 | 65 | 65 | Link |
Nation | 14 | 55 | 1592 | 199 | 201 | Link |
NELL23K | 22925 | 200 | 25445 | 4961 | 4952 | Link |
NELL-995 | 75492 | 200 | 126176 | 5000 | 5000 | Link |
OpenBioLink | 184765 | 28 | 4192002 | 188394 | 183011 | Link |
ogbl-biokg | 93773 | 51 | 4762678 | 162886 | 162780 | Link |
ogbl-wikikg2 | 2500604 | 535 | 16109182 | 429456 | 598543 | Link |
OpenBG500 | 249743 | 500 | 1242550 | 5000 | 5000 | Link |
OpenBG500-L | 2782223 | 500 | 47410032 | 10000 | 10000 | Link |
Sport | 1039 | 4 | 1349 | 358 | 358 | Link |
Toy | 280 | 112 | 4565 | 109 | 152 | Link |
UMLS | 135 | 46 | 5216 | 652 | 661 | Link |
UMLS-PubMed | 59226 | 443 | 2030841 | 8756 | 8689 | Link |
WD-singer | 10282 | 135 | 16142 | 2163 | 2203 | Link |
WN11 | 38696 | 11 | 110361 | 5212 | 21035 | Link |
WN18 | 40943 | 18 | 141442 | 5000 | 5000 | Link |
WN18RR | 40943 | 11 | 86835 | 2924 | 2824 | Link |
wikidata5m | 4594485 | 822 | 20614279 | 5163 | 5163 | Link |
YAGO3-10 | 123182 | 37 | 1079040 | 4978 | 4982 | Link |
YAGO37 | 123189 | 37 | 420623 | 50000 | 50000 | Link |
M-/YAGO39K | 85484 | 39 | 354997 | 9341 | 9364 | Link |
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>Temporal KGR Datasets <span id="temporal-knowledge-graphs-"></span>
Dataset | # Entities | # Relations | # Timestamps | # Train Triplets | # Val. Triplets | # Test Triplets | # Description |
---|---|---|---|---|---|---|---|
DBpedia-3SP | 66967 | 968 | 3 | 103211 | 3000 | - | Link |
GDELT | 7691 | 240 | 8925 | 1033270 | 238765 | 305241 | Link |
GDELT-small | 500 | 20 | 366 | 2735685 | 341961 | 341961 | Link |
GDELT-m10 | 50 | 20 | 30 | 221132 | 27608 | 27926 | Link |
IMDB-13-3SP | 3244455 | 14 | 3 | 7913773 | 10000 | - | Link |
IMDB-30SP | 243148 | 14 | 30 | 621096 | 3000 | 3000 | Link |
ICSES05-15 | 10488 | 251 | 4017 | 386962 | 46092 | 46275 | Link |
ICEWS11-14 | 6738 | 235 | 1461 | 118766 | 14859 | 14756 | Link |
ICSES14 | 7128 | 230 | 365 | 63685 | 13823 | 13222 | Link |
ICEWS14-Plus | 7128 | 230 | 365 | 72826 | 8941 | 8963 | Link |
ICEWS18 | 23033 | 256 | 7272 | 373018 | 45995 | 49545 | Link |
YAGO11k/YOGA | 10623 | 10 | 189 | 161540 | 19523 | 20026 | Link |
YAGO-3SP | 27009 | 37 | 3 | 124757 | 3000 | 3000 | Link |
YAGO15k | 15403 | 34 | 198 | 110441 | 13815 | 13800 | Link |
YAGO1830 | 10038 | 10 | 205 | 51205 | 10973 | 10973 | Link |
WIKI/Wikidata12k | 12554 | 24 | 232 | 2735685 | 341961 | 341961 | Link |
Wikidata11k | 11134 | 95 | 328 | 242844 | 28748 | 14283 | Link |
Wikidata-big | 125726 | 203 | 1700 | 323635 | 5000 | 5000 | Link |
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># Val. Triplets</th> <th># Test. Triplets</th> <th># 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>Static Knowledge Graph Reasoning <span id="static-knowledge-graph-reasoning-"></span>
Translational Models <span id="translational-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | GoldE | Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization | ICML | Transductive | Link | Link |
2023 | CompoundE3D | Knowledge Graph Embedding with 3D Compound Geometric Transformations | arXiv | Transductive | Link | - |
2023 | EXPRESSIVE | EXPRESSIVE: A SPATIO-FUNCTIONAL EMBEDDING FOR KNOWLEDGE GRAPH COMPLETION | ICLR | Transductive | Link | Link |
2022 | DualDE | DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning | WSDM | Transductive | Link | - |
2022 | TripleRE | TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors | arXiv | Transductive | Link | - |
2022 | InterHT | InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities | arXiv | Transductive | Link | - |
2022 | HousE | HousE: Knowledge Graph Embedding with Householder Parameterization | ICML | Transductive | Link | Link |
2022 | ReflectE | Knowledge graph embedding by reflection transformation | KBS | Transductive | link | - |
2022 | DensE | DensE: An enhanced non-commutative representation for knowledge graph embedding with adaptive semantic hierarchy | NC | Transductive | link | - |
2022 | StructurE | Structural context-based knowledge graph embedding for link prediction | NC | Transductive | link | - |
2021 | HA-RotatE | Hierarchical-aware relation rotational knowledge graph embedding for link prediction | NC | Transductive | link | - |
2021 | PairRE | PairRE: Knowledge Graph Embeddings via Paired Relation Vectors | ACL | Transductive | link | link |
2021 | CyclE | Cycle or Minkowski: Which is More Appropriate for Knowledge Graph Embedding | CIKM | Transductive | link | - |
2021 | MöbiusE | MöbiusE: Knowledge Graph Embedding on Möbius Ring | KBS | Transductive | link | link |
2021 | 5*E | 5 Knowledge Graph Embeddings with Projective Transformations* | AAAI | Transductive | link | - |
2021 | BiQUE | BiQUE: Biquaternionic Embeddings of Knowledge Graphs | EMNLP | Transductive | link | link |
2021 | HBE | Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction | EMNLP | Transductive | link | - |
2021 | RotL | Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings | EMNLP | Transductive | link | - |
2021 | GrpKG | Knowledge Graph Representation Learning as Groupoid: Unifying TransE, RotatE, QuatE, ComplEx | CIKM | Transductive | link | - |
2021 | MQuadE | MQuadE: a Unified Model for Knowledge Fact Embedding | WWW | Transductive | link | - |
2020 | ConnectE | Knowledge graph entity typing via learning connecting embeddings | KBS | Transductive | link | - |
2020 | MAKR | An asymmetric knowledge representation learning in manifold space | IS | Transductive | link | - |
2020 | HAKE | Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction | AAAI | Transductive | link | link |
2020 | BoxE | BoxE: A Box Embedding Model for Knowledge Base Completion | NeurIPS | Transductive | link | - |
2020 | OTE | Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding | ACL | Transductive | link | link |
2020 | TransRHS | TransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure | IJCAI | Transductive | link | link |
2020 | MDE | MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs | ECAI | Transductive | link | link |
2020 | AprilE | AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding | COLING | Transductive | link | - |
2020 | RatE | RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion | COLING | Transductive | link | - |
2020 | Rotate3D | Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for Knowledge Graph Embedding | CIKM | Transductive | link | link |
2020 | LineaRE | LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction | ICDM | Transductive | link | Link |
2020 | GeomE | Knowledge Graph Embeddings in Geometric Algebras | COLING | Transductive | link | - |
2020 | SpacEss | Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them | ISWC | Transductive | link | - |
2020 | HyperKG | Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion | ESWC | Transductive | link | link |
2019 | RotatE | RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space | ICLR | Transductive | link | link |
2019 | TransGate | TransGate: Knowledge Graph Embedding with Shared Gate Structure | AAAI | Transductive | link | - |
2019 | TransMS | TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics | IJCAI | Transductive | link | - |
2019 | TransW | Composing Knowledge Graph Embeddings via Word Embeddings | arXiv | Inductive | link | - |
2019 | MuRP | Multi-relational Poincaré Graph Embeddings | NeurIPS | Transductive | link | link |
2018 | TransAt | Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism | IJCAI | Transductive | link | link |
2018 | TorusE | TorusE: Knowledge Graph Embedding on a Lie Group | AAAI | Transductive | Link | link |
2018 | TransC | Differentiating Concepts and Instances for Knowledge Graph Embedding | EMNLP | Transductive | link | link |
2017 | puTransE | Non-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge Graphs | AAAI | Transductive | link | - |
2017 | ITransF | An Interpretable Knowledge Transfer Model for Knowledge Base Completion | ACL | Transductive | link | - |
2017 | CombinE | Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations | CIKM | Transductive | link | - |
2017 | Trans-RS | Learning Knowledge Embeddings by Combining Limit-based Scoring Loss | CIKM | Transductive | link | - |
2016 | TransA | Locally Adaptive Translation for Knowledge Graph Embedding | AAAI | Transductive | link | - |
2016 | TranSparse | Knowledge Graph Completion with Adaptive Sparse Transfer Matrix | AAAI | Transductive | link | - |
2016 | TransG | TransG: A Generative Model for Knowledge Graph Embedding | ACL | Transductive | link | link |
2016 | ManifoldE | From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction | IJCAI | Transductive | link | link |
2016 | FT | Knowledge Graph Embedding by Flexible Translation | KR | Transductive | link | link |
2016 | lppTrans | A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations | NAACL-HLT | Transductive | link | link |
2016 | STransE | STransE: A Novel Embedding Model of Entities and Relationships in Knowledge Bases | NAACL-HLT | Transductive | link | link |
2015 | TransD | Knowledge Graph Embedding via Dynamic Mapping Matrix | ACL | Transductive | link | - |
2015 | TransR | Learning Entity and Relation Embeddings for Knowledge Graph Completion | AAAI | Transductive | link | link |
2015 | RTransE | Composing Relationships with Translations | EMNLP | Transductive | link | link |
2015 | KG2E | Learning to Represent Knowledge Graphs with Gaussian Embedding | CIKM | Transductive | link | - |
2014 | TransM | Transition-based Knowledge Graph Embedding with Relational Mapping Properties | PACLIC | Transductive | link | - |
2014 | TransH | Knowledge Graph Embedding by Translating on Hyperplanes | AAAI | Transductive | link | - |
2013 | TransE | Translating Embeddings for Modeling Multi-relational Data | NeurIPS | Transductive | link | - |
Tensor Decompositional Models <span id="tensor-decompositional-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | NestE | NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning | AAAI | Transductive | Link | Link |
2024 | CompilE | Modeling Knowledge Graphs with Composite Reasoning | AAAI | Transductive | Link | Link |
2022 | QuatRE | QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings | WWW | Transductive | Link | Link |
2022 | GIE | Geometry Interaction Knowledge Graph Embeddings | AAAI | Transductive | Link | Link |
2021 | HopfE | HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations | CIKM | Transductive | link | link |
2021 | DualE | Dual Quaternion Knowledge Graph Embeddings | AAAI | Transductive | link | link |
2020 | SEEK | SEEK: Segmented Embedding of Knowledge Graphs | ACL | Transductive | Link | Link |
2020 | LowFER | LowFER: Low-rank Bilinear Pooling for Link Prediction | ICML | Transductive | Link | Link |
2020 | B-CP | A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings | EMNLP | Transductive | Link | - |
2020 | BTDE | BTDE: Block Term Decomposition Embedding for Link Prediction in Knowledge Graph | ECAI | Transductive | Link | - |
2020 | MEI | Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion | ECAI | Transductive | Link | Link |
2019 | QuatE | Quaternion Knowledge Graph Embeddings | NeurIPS | Transductive | Link | Link |
2019 | DihEdral | Relation Embedding with Dihedral Group in Knowledge Graph | ACL | Transductive | Link | - |
2019 | TuckER | TuckER: Tensor Factorization for Knowledge Graph Completion | EMNLP | Transductive | Link | Link |
2019 | CrossE | Interaction Embeddings for Prediction and Explanation in Knowledge Graphs | WSDM | Transductive | Link | Link |
2018 | HOLEX | Expanding Holographic Embeddings for Knowledge Completion | NeurIPS | Transductive | Link | - |
2018 | SimplE | SimplE Embedding for Link Prediction in Knowledge Graphs | NeurIPS | Transductive | Link | Link |
2017 | ANALOGY | Analogical Inference for Multi-relational Embeddings | ICML | Transductive | Link | Link |
2016 | HolE | Holographic Embeddings of Knowledge Graphs | AAAI | Transductive | Link | Link |
2016 | ComplEx | Complex Embeddings for Simple Link Prediction | ICML | Transductive | Link | Link |
2015 | DISTMULT | Embedding Entities and Relations for Learning and Inference in Knowledge Bases | ICLR | Transductive | Link | - |
2011 | RESCAL | A Three-Way Model for Collective Learning on Multi-Relational Data | ICML | Transductive | Link | Link |
Neural Network Models <span id="neural-network-models-"></span>
Tranditional NN Models <span id="traditonal-neural-network-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | LAFA | LAFA: Multimodal Knowledge Graph Completion with Link Aware Fusion and Aggregation | AAAI | Transductive | Link | Link |
2017 | ProjE | ProjE: Embedding Projection for Knowledge Graph Completion | AAAI | Transductive | Link | Link |
2016 | NAM | Probabilistic Reasoning via Deep Learning: Neural Association Models | arXiv | Transductive | Link | - |
2013 | SME | A semantic matching energy function for learning with multi-relational data | Machine Learning | Transductive | Link | - |
2013 | NTN | Reasoning With Neural Tensor Networks for Knowledge Base Completion | NeurIPS | Transductive | Link | - |
CNN Models <span id="convolutional-neural-network-models-"></span>
Year | Model Name | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2023 | ConvRot | Knowledge graph embedding by relational rotation and complex convolution for link prediction | Expert Syst Appl | Transductive | Link | Link |
2021 | ConE | ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs | NeurIPS | Transductive | Link | - |
2021 | ConEx | Convolutional Complex Knowledge Graph Embeddings | ESWC | Transductive | Link | Link |
2020 | InteractE | InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions | AAAI | Transductive | Link | Link |
2019 | ConvR | Adaptive Convolution for Multi-Relational Learning | NAACL-HLT | Transductive | Link | - |
2019 | HypER | Hypernetwork Knowledge Graph Embeddings | ICANN | Transductive | Link | - |
2018 | ConvKB | A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network | NAACL-HLT | Transductive | Link | Link |
2018 | ConvE | Convolutional 2D Knowledge Graph Embeddings | AAAI | Transductive | Link | Link |
GNN Models <span id="graph-neural-network-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | HyRel | RHKH: Relational Hypergraph Neural Network for Link Prediction on N-ary Knowledge Hypergraph | ACM MM | Transductive | Link | Link |
2024 | HyRel | Generalize to Fully Unseen Graphs: Learn Transferable Hyper-Relation Structures for Inductive Link Prediction | ACM MM | Transductive | Link | Link |
2024 | MGTCA | Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion | AAAI | Transductive | Link | - |
2024 | MINES | MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs | AAAI | Inductive | Link | - |
2023 | C-MPNN | A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs | NeurIPS | Inductive | Link | Link |
2023 | AdaProp | AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning | KDD | Inductive | Link | Link |
2023 | InGram | InGram: Inductive Knowledge Graph Embedding via Relation Graphs | ICML | Inductive | Link | Link |
2023 | REPORT | Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformer | arXiv | Inductive | Link | - |
2023 | DEKG-ILP | Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction | ICDE | Inductive | Link | Link |
2023 | RMPI | Relational Message Passing for Fully Inductive Knowledge Graph Completion | ICDE | Inductive | Link | Link |
2023 | TransEQ | TWO BIRDS, ONE STONE: AN EQUIVALENT TRANSFORMATION FOR HYPER-RELATIONAL KNOWLEDGE GRAPH MODELING | arXiv | Transductive | Link | - |
2022 | LogCo | Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations | EMNLP | Inductive | Link | Link |
2022 | NoGE | Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction | WSDM | Transductive | Link | - |
2022 | SGI | Subgraph Representation Learning with Hard Negative Samples for Inductive Link Prediction | ICASSP | Inductive | Link | - |
2022 | RED-GNN | Knowledge Graph Reasoning with Relational Digraph | WWW | Inductive | Link | Link |
2022 | ConGLR | Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction | SIGIR | Inductive | Link | - |
2022 | CFAG | Exploring Relational Semantics for Inductive Knowledge Graph Completion | AAAI | Inductive | Link | - |
2022 | IKGE | Open-world knowledge graph completion for unseen entities and relations via attentive feature aggregation | IS | Inductive | Link | - |
2022 | MorsE | Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding | SIGIR | Inductive | Link | Link |
2022 | BERTRL | Inductive Relation Prediction by BERT | AAAI | Inductive | Link | Link |
2022 | CBGNN | CYCLE REPRESENTATION LEARNING FOR INDUCTIVE RELATION PREDICTION | ICLR Worshop | Inductive | Link | - |
2022 | SNRI | Subgraph Neighboring Relations Infomax for Inductive Link Prediction | IJCAI | Inductive | Link | Link |
2022 | TEMP | Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs | arXiv | Inductive | Link | Link |
2022 | Meta-iKG | Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning | TKDE | Inductive | Link | - |
2022 | CSR | Few-shot Relational Reasoning via Connection Subgraph Pretraining | NeurIPS | Inductive | Link | Link |
2021 | KE-GCN | Knowledge Embedding Based Graph Convolutional Network | WWW | Transductive | Link | Link |
2021 | DisenKGAT | DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network | CIKM | Transductive | Link | Link |
2021 | M2GNN | Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion | WWW | Transductive | Link | - |
2021 | HRFN | Meta-Learning Based Hyper-Relation Feature Modeling for Out-of-Knowledge-Base Embedding | CIKM | Inductive | Link | - |
2021 | GEN | Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction | NeurIPS | Inductive | Link | Link |
2021 | SRGCN | SRGCN: Graph-based multi-hop reasoning on knowledge graphs | NC | Transductive | Link | - |
2021 | TRAR | Target relational attention-oriented knowledge graph reasoning | NC | Transductive | Link | - |
2021 | KompaRe | KompaRe: A Knowledge Graph Comparative Reasoning System | KDD | Transductive | Link | - |
2021 | INDIGO | INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding | NeurIPS | Inductive | Link | - |
2021 | NBF-Net | Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction | NeurIPS | Inductive | Link | Link |
2021 | CoMPILE | Communicative Message Passing for Inductive Relation Reasoning | AAAI | Inductive | Link | Link |
2021 | TACT | Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs | AAAI | Inductive | Link | Link |
2021 | RPC-IR | Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning | TNNLS | Inductive | Link | - |
2020 | DPMPN | Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning | ICLR | Transductive | Link | Link |
2020 | RGHAT | Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion | AAAI | Transductive | Link | - |
2020 | GAEAT | GAEAT: Graph Auto-Encoder Attention Networks for Knowledge Graph Completion | cikm | Transductive | Link | - |
2020 | COMPGCN | Composition-based Multi-Relational Graph Convolutional Networks | ICLR | Transductive | Link | Link |
2020 | GraIL | Inductive Relation Prediction by Subgraph Reasoning | ICML | Inductive | Link | Link |
2019 | M-GNN | Robust Embedding with Multi-Level Structures for Link Prediction | IJCAI | Transductive | Link | - |
2019 | SACN | End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion | AAAI | Transductive | Link | Link |
2019 | A2N | A2N: Attending to Neighbors for Knowledge Graph Inference | ACL | Transductive | Link | - |
2019 | KBGAT | Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs | ACL | Transductive | Link | Link |
2019 | TransGCN | TransGCN: Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction | K-CAP | Transductive | Link | - |
2019 | LAN | Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding | AAAI | Inductive | Link | Link |
2018 | RGCN | Modeling Relational Data with Graph Convolutional Networks | ESWC | Transductive | Link | Link |
2017 | - | Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach | IJCAI | Inductive | Link | Link |
Transformer Models <span id="transformer-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | KnowFormer | KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning | ICML | Inductive | Link | - |
2023 | iHT | Pre-training Transformers for Knowledge Graph Completion | arXiv | Inductive | Link | - |
2023 | LambdaKG | LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings | arXiv | Transductive | Link | Link |
2022 | Ruleformer | Ruleformer: Context-aware Rule Mining over Knowledge Graph | COLING | Transductive | Link | Link |
2022 | kgTransformer | Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries | KDD | Inductive | Link | Link |
2022 | Kformer | Kformer: Knowledge Injection in Transformer Feed-Forward Layers | NLPCC | Transductive | Link | Link |
2022 | TET | Transformer-based Entity Typing in Knowledge Graphs | EMNLP | Link | Link | |
2022 | KPGT | KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction | COLING | Transductive | Link | - |
2021 | HittER | HittER: Hierarchical Transformers for Knowledge Graph Embeddings | EMNLP | Link | Link | |
2020 | - | Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models | COLING | Transductive | Link | - |
2020 | CoKE | CoKE: Contextualized Knowledge Graph Embedding | arXiv | Transductive | Link | Link |
2020 | KG-BERT | KG-BERT: BERT for knowledge graph completion | AAAI | Transductive | Link | Link |
2020 | CODEX | CoDEx: A Comprehensive Knowledge Graph Completion Benchmark | EMNLP | Link | Link | |
2019 | ATOMIC | ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning | AAAI | Link | Link | |
2023 | KG-R3 | A Retrieve-and-Read Framework for Knowledge Graph Link Prediction | CIKM | Transductive | Link | Link |
Path-based Models <span id="path-based-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2023 | AstarNet | AstarNet: A Scalable Path-based Reasoning Approach for Knowledge Graphs | NeurIPS | Transductive | Link | Link |
2022 | CURL | Learning to Walk with Dual Agents for Knowledge Graph Reasoning | AAAI | Transductive | Link | Link |
2019 | CPL | Collaborative policy learning for open knowledge graph reasoning | EMNLP | Transductive | Link | Link |
2018 | DIVA | Variational Knowledge Graph Reasoning | NAACL | Transductive | Link | - |
2018 | MINERVA | Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning | ICLR | Transductive | Link | Link |
2018 | Multi-Hop | Multi-Hop Knowledge Graph Reasoning with Reward Shaping | EMNLP | Transductive | Link | Link |
2018 | M-Walk | M-Walk: Learning to walk over graphs using monte carlo tree search | NeurIPS | Transductive | Link | Link |
2017 | LogSumExp | Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks | EACL | Transductive | Link | Link |
2017 | DeepPath | DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning | EMNLP | Transductive | Link | Link |
2015 | RNNPRA | Compositional Vector Space Models for Knowledge Base Completion | AAAI | Transductive | Link | - |
2014 | ProPPR | Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases | EMNLP | Transductive | Link | - |
2010 | PRA | Relational retrieval using a combination of path-constrained random walks | Machine Learning | Transductive | Link | - |
Rule-based Models <span id="rule-based-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | FIT | RETHINKING COMPLEX QUERIES ON KNOWLEDGE GRAPHS WITH NEURAL LINK PREDICTORS | ICLR | Transductive | Link | Link |
2023 | DiffLogic | Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs | NeurIPS | Inductive | Link | Link |
2023 | NCRL | NEURAL COMPOSITIONAL RULE LEARNING FOR KNOWLEDGE GRAPH REASONING | ICLR | Inductive | Link | Link |
2023 | RulE | Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding | arXiv | Inductive | Link | Link |
2023 | LSTK | Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion | NeurIPS | Inductive | Link | Link |
2022 | RF | Dynamic knowledge graph reasoning based on deep reinforcement learning | Knowledge-Based Systems | Transductive | Link | Link |
2022 | RLogic | RLogic: Recursive Logical Rule Learning from Knowledge Graphs | KDD | Inductive | Link | - |
2021 | RNNLOGIC | RNNLOGIC: LEARNING LOGIC RULES FOR REASONING ON KNOWLEDGE GRAPHS | ICLR | Inductive | Link | Link |
2020 | Neural-Num-LP | Differentiable learning of numerical rules in knowledge graphs | ICLR | Inductive | Link | - |
2020 | ExpressGNN | Efficient probabilistic logic reasoning with graph neural networks | ICLR | Inductive | Link | Link |
2019 | IterE | Iteratively learning embeddings and rules for knowledge graph reasoning, | WWW | Inductive | Link | Link |
2019 | pLogicNet | Probabilistic logic neural networks for reasoning | NeurIPS | Inductive | Link | Link |
2019 | DRUM | DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs | NeurIPS | Inductive | Link | Link |
2019 | RLvLR | An Embedding-based Approach to Rule Learning in Knowledge Graphs | TKDE | Inductive | Link | - |
2018 | RuleN | Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion | ISWC | Inductive | Link | - |
2018 | RUGE | Knowledge graph embedding with iterative guidance from soft rules | AAAI | Inductive | Link | Link |
2017 | NTP | End-to-end differentiable proving | NeurIPS | Inductive | Link | - |
2017 | NeuralLP | Differentiable learning of logical rules for knowledge base reasoning | NeurIPS | Inductive | Link | Link |
2016 | KALE | Jointly embedding knowledge graphs and logical rules | EMNLP | Inductive | Link | Link |
2013 | AMIE | AMIE: association rule mining under incomplete evidence in ontological knowledge bases | WWW | Inductive | Link | Link |
Temporal Knowledge Graph Reasoning <span id="temporal-knowledge-graph-reasoning-"></span>
Evaluation Papers <span id="rnn-based-models-"></span>
Year | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|
2022 | On the Evaluation of Methods for Temporal Knowledge Graph Forecasting | NeurIPS | Extrapolation | Link | Link |
RNN-based Models <span id="rnn-based-models-"></span>
Quadruple-based Models <span id="quadruple-based-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | ECEformer | Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph | SIGIR | Extrapolation | Link | Link |
2024 | TEILP | TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning | AAAI | Interpolation | Link | Link |
2023 | TILP | TILP: DIFFERENTIABLE LEARNING OF TEMPORAL LOGICAL RULES ON KNOWLEDGE GRAPHS | ICLR | Interpolation | Link | Link |
2022 | EvoKG | EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs | WSDM | Extrapolation | Link | Link |
2021 | Tpmod | TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion | TKDD | Interpolation | Link | Link |
2021 | HIP | HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph | IJCAI | Extrapolation | Link | Link |
2020 | TeMP | TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion | EMNLP | Interpolation | Link | Link |
2018 | TTransE | Learning Sequence Encoders for Temporal Knowledge Graph Completion | EMNLP | Interpolation | Link | Link |
2018 | TA-DISTMULT | Learning Sequence Encoders for Temporal Knowledge Graph Completion | EMNLP | Interpolation | Link | Link |
2018 | TA-TransE | Learning Sequence Encoders for Temporal Knowledge Graph Completion | EMNLP | Interpolation | Link | Link |
2017 | Know-Evolve | Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs | ICML | Extrapolation | Link | Link |
Path-based Models <span id="path-based-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | TILR | Temporal inductive logic reasoning | IJCAI | Interpolation | Link | Link |
2022 | ExKGR | ExKGR: Explainable Multi-hop Reasoning for Evolving Knowledge Graph | DASFAA | Interpolation | Link | Link |
2021 | TimeTraveler | TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting | EMNLP | Extrapolation | Link | Link |
2021 | CluSTeR | Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs | ACL | Extrapolation | Link | Link |
2021 | Tpath | Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning | Applied Soft Computing | Interpolation | Link | Link |
2020 | DacKGR | Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph | EMNLP | Interpolation | Link | Link |
Graph-based Models <span id="graph-based-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | IncDE | Towards Continual Knowledge Graph Embedding via Incremental Distillation | AAAI | - | Link | Link |
2023 | HGLS | Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning | WWW | Extrapolation | Link | Link |
2023 | LOGAE-TKG | Logistics Audience Expansion via Temporal Knowledge Graph | CIKM | Interpolation | Link | Link |
2023 | LogCL | Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning | ICDE | Interpolation | Link | Link |
2023 | RETIA | RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation | ICDE | Extrapolation | Link | Link |
2023 | RPC | Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning | SIGIR | Extrapolation | Link | Link |
2023 | HiSMatch | HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning | arXiv | Extrapolation | Link | Link |
2022 | TiRGN | TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning | IJCAI | Extrapolation | Link | Link |
2022 | TRHyTE | TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes | DASFAA | Interpolation | Link | Link |
2021 | RE-GCN | Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning | SIGIR | Extrapolation | Link | Link |
2020 | EvolveGCN | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs | AAAI | Extrapolation | Link | Link |
2020 | RE-NET | Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs | EMNLP | Extrapolation | Link | Link |
RNN-agnostic Models <span id="rnn-agnostic-models-"></span>
Time-Vector Guided Models <span id="time-vector-guided-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | IME | IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion | WWW | Extrapolation | Link | - |
2023 | DREAM | DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning | SIGIR | Extrapolation | Link | - |
2022 | TLT-KGE | Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion | CIKM | Interpolation | Link | Link |
2022 | TempoQR | TempoQR: Temporal Question Reasoning over Knowledge Graphs | AAAI | Interpolation | Link | Link |
2022 | BoxTE | Temporal Knowledge Graph Completion Using Box Embeddings | AAAI | Interpolation | Link | Link |
2022 | TuckERTNT | Tucker decomposition-based temporal knowledge graph completion | KBS | Interpolation | Link | - |
2022 | RotateQVS | RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion | ACL | Interpolation | Link | - |
2021 | ChronoR | ChronoR: Rotation Based Temporal Knowledge Graph Embedding | AAAI | Interpolation | Link | - |
2021 | DBKGE | Learning Dynamic Embeddings for Temporal Knowledge Graphs | WSDM | Interpolation | Link | - |
2021 | CyGNet | Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks | AAAI | Extrapolation | Link | Link |
2021 | T-GAP | Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion | SIGIR | Interpolation | Link | Link |
2021 | - | Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework | EMNLP | Interpolation | Link | - |
2021 | xERTE | Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs | ICLR | Extrapolation | Link | Link |
2020 | - | Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision | CIKM | Interpolation | Link | Link |
2020 | TNTComplEx | Tensor Decompositions for Temporal Knowledge Base Completion | ICLR | Interpolation | Link | Link |
2018 | - | Deriving Validity Time in Knowledge Graph | WWW | Interpolation | Link | - |
Time-Operation Guided Models <span id="time-operation-guided-models-"></span>
Year | Model | Title | Venue | Scenario | Paper | Code |
---|---|---|---|---|---|---|
2024 | HGE | HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces | AAAI | Interpolation | Link | Link |
2024 | IncDE | Towards Continual Knowledge Graph Embedding via Incremental Distillation | AAAI | Extrapolation | Link | - |
2024 | TEILP | TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning | AAAI | Interpolation | Link | Link |
2023 | CENET | Temporal Knowledge Graph Reasoning with Historical Contrastive Learnings | AAAI | Extrapolation | Link | - |
2022 | GHT | Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs | ACL | Extrapolation | Link | - |
2022 | DA-Net | DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasonings | CIKM | Extrapolation | Link | - |
2022 | MetaTKGR | Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs | arXiv | Extrapolation | Link | - |
2022 | rGalT | Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure | IJCAI | Extrapolation | Link | - |
2022 | FILT | Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information | AKBC | Interpolation | Link | - |
2022 | TKGC-AGP | Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding | COLING | Interpolation | Link | - |
2022 | Tlogic | TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge | AAAI | Extrapolation | Link | Link |
2022 | DKGE | Efficiently embedding dynamic knowledge graphs | KBS | Interpolation | Link | Link |
2022 | CEN | Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning | ACL | Extrapolation | Link | Link |
2021 | TPRec | Time-aware Path Reasoning on Knowledge Graph for Recommendations | TOIS | Extrapolation | Link | - |
2021 | - | Spatial-Temporal Attention Network for Temporal Knowledge Graph Completion | DASFAA | Interpolation | Link | - |
2021 | TIE | TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion | SIGIR | Interpolation | Link | Link |
2021 | TeLM | Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings | NAACL | Interpolation | Link | Link |
2021 | RTFE | RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion | NAACL | Interpolation | Link | - |
2021 | TANGO | Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs | EMNLP | Extrapolation | Link | Link |
2020 | DyERNIE | DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion | EMNLP | Interpolation | Link | - |
2020 | - | Explainable Link Prediction for Emerging Entities in Knowledge Graphs | ISWC | Interpolation | Link | Link |
2020 | TDGNN | Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network | WWW | Extrapolation | Link | Link |
2020 | - | Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction | EMNLP | Extrapolation | Link | - |
2020 | ATiSE | Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding | ISWC | Interpolation | Link | Link |
2020 | Diachronic embeddings | Diachronic Embedding for Temporal Knowledge Graph Completion | AAAI | Interpolation | Link | Link |
2020 | TeRo | TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation | COLING | Interpolation | Link | Link |
2019 | DyRep | DyRep: Learning Representations over Dynamic Graphs | ICLR | Extrapolation | Link | - |
2018 | HyTE | HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding | EMNLP | Interpolation | Link | - |
2018 | ChronoTranslate | Temporal reasoning over event knowledge graphs | KBCOM | Interpolation | Link | - |
Multi-Modal Knowledge Graph Reasoning <span id="multi-modal-knowledge-graph-reasoning-"></span>
Transformer-agnostic Model <span id="transformer-agnostic-models-"></span>
Year | Model | Title | Venue | Paper | Code |
---|---|---|---|---|---|
2023 | -- | Multimodal Biological Knowledge Graph Completion via Triple Co-attention Mechanism | ICDE | Link | - |
2022 | MMKGR | MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning | arXiv | Link | - |
2022 | MMKRL | MMKRL: A robust embedding approach for multi-modal knowledge graph representation learning | Applied Intelligence | Link | - |
2022 | MKGRL-MS | Multi-modal knowledge graphs representation learning via multi-headed self-attention | Information Fusion | Link | - |
2022 | ZSMMKG | Improving Zero-Shot Phrase Grounding via Reasoning on External Knowledge and Spatial Relations | AAAI | Link | - |
2022 | OTKGE | OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport | NeurIPS | Link | Link |
2022 | MM-RNS | Relation-enhanced Negative Sampling for Multimodal Knowledge Graph Completion | MM | Link | - |
2022 | CKGC | Cross-modal Knowledge Graph Contrastive Learning for Machine Learning Method Recommendation | MM | Link | - |
2022 | MoSE | MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion | EMNLP | Link | Link |
2022 | MCLEA | Multi-modal Contrastive Representation Learning for Entity Alignment | COLING | Link | Link |
2021 | - | Explicit Knowledge Incorporation for Visual Reasoning | CVPR | Link | - |
2021 | EVA | Visual Pivoting for (Unsupervised) Entity Alignment | AAAI | Link | Link |
2021 | HMEA | Multi-modal Entity Alignment in Hyperbolic Space | arXiv | Link | - |
2021 | RSME | Is Visual Context Really Helpful for Knowledge Graph? A Representation Learning Perspective | MM | Link | Link |
2020 | MKGAT | Multi-modal Knowledge Graphs for Recommender Systems | CIKM | Link | - |
2020 | Mucko | Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering | IJCAI | Link | Link |
2020 | GRUC | Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering | PR | Link | - |
2020 | - | Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations | arXiv | Link | - |
2020 | MMEA | MMEA: Entity Alignment for Multi-modal Knowledge Graph | ICKSEM | Link | - |
2020 | MRCGN | End-to-End Entity Classification on Multimodal Knowledge Graphs | arXiv | Link | Link |
2019 | GQA | GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering | CVPR | Link | Link |
2019 | VSUA | Aligning Linguistic Words and Visual Semantic Units for Image Captioning | MM | Link | Link |
2019 | KVQA | KVQA: Knowledge-Aware Visual Question Answering | AAAI | Link | Link |
2019 | - | Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs | AKBC | Link | Link |
2019 | - | MMKG: Multi-Modal Knowledge Graphs | ESWC | Link | Link |
2019 | MKRL | Knowledge representation learning with entity descriptions, hierarchical types, and textual relations | Information Processing & Management | Link | - |
2019 | TransAE | Multimodal Data Enhanced Representation Learning for Knowledge Graphs | ACL | Link | Link |
2018 | MKBE | Embedding Multimodal Relational Data for Knowledge Base Completion | ACL | Link | Link |
2018 | MTRL | A multimodal translation-based approach for knowledge graph representation learning | SEM | Link | Link |
2018 | KR-AMD | Representation learning of knowledge graphs with entity attributes and multimedia descriptions | BigMM | Link | - |
2017 | KB-VQA | Explicit Knowledge-based Reasoning for Visual Question Answering | IJCAI | Link | - |
2017 | KBLRN | KBLRN: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features | AUAI | Link | - |
2016 | IKRL | Image-embodied Knowledge Representation Learning | IJCAI | Link | Link |
2016 | DKRL | Representation Learning of Knowledge Graphs with Entity Descriptions | AAAI | Link | Link |
2016 | CKE | Collaborative Knowledge Base Embedding for Recommender Systems | SIGKDD | Link | - |
Transformer-based Model <span id="transformer-based-models-"></span>
Year | Model | Title | Venue | Paper | Code |
---|---|---|---|---|---|
2023 | AIR | AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities | CIKM | Link | Link |
2023 | SGMPT | Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning | ArXiv | Link | Link |
2023 | IMF | IMF: Interactive Multimodal Fusion Model for Link Prediction | WWW | Link | Link |
2022 | DRAGON | Deep Bidirectional Language-Knowledge Graph Pretraining | NeurIPS | Link | Link |
2022 | MuKEA | MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering | CVPR | Link | Link |
2022 | MSNEA | Multi-modal Siamese Network for Entity Alignment | KDD | Link | Link |
2022 | HRGAT | Hyper-node Relational Graph Attention Network for Multi-modal Knowledge Graph Completion | ACM Transactions on Multimedia Computing, Communications, and Applications | Link | Link |
2022 | KPGT | KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction | KDD | Link | Link |
2022 | MKGformer | Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion | SIGIR | Link | Link |
2022 | MarT | Multimodal Analogical Reasoning over Knowledge Graphs | ICLR | Link | Link |
2022 | Knowledge-CLIP | Contrastive Language-Image Pre-Training with Knowledge Graphs | NeurIPS | Link | - |
2022 | TMEG | Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension | ACL | Link | - |
2022 | VBKGC | Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling | KDD | Link | - |
2021 | Krisp | Krisp: Integrating implicit and symbolic knowledge for open domain knowledge-based vqa | CVPR | Link | - |