woojeongjin / Dynamic Kg
Dynamic (Temporal) Knowledge Graph Completion (Reasoning)
Stars: ✭ 381
Projects that are alternatives of or similar to Dynamic Kg
Grakn
TypeDB: a strongly-typed database
Stars: ✭ 2,947 (+673.49%)
Mutual labels: knowledge-graph, knowledge-base
Ccks2019 el
CCKS 2019 中文短文本实体链指比赛技术创新奖解决方案
Stars: ✭ 326 (-14.44%)
Mutual labels: knowledge-graph, knowledge-base
Zincbase
A batteries-included kit for knowledge graphs
Stars: ✭ 249 (-34.65%)
Mutual labels: knowledge-graph, knowledge-base
Topic Db
TopicDB is a topic maps-based semantic graph store (using PostgreSQL for persistence)
Stars: ✭ 164 (-56.96%)
Mutual labels: knowledge-graph, knowledge-base
Logseq
A privacy-first, open-source platform for knowledge management and collaboration. Desktop app download link: https://github.com/logseq/logseq/releases, roadmap: https://trello.com/b/8txSM12G/roadmap
Stars: ✭ 8,210 (+2054.86%)
Mutual labels: knowledge-base, knowledge-graph
Aser
ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data.
Stars: ✭ 171 (-55.12%)
Mutual labels: knowledge-graph, knowledge-base
Graphbrain
Language, Knowledge, Cognition
Stars: ✭ 294 (-22.83%)
Mutual labels: knowledge-graph, knowledge-base
Capse
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (NAACL 2019)
Stars: ✭ 114 (-70.08%)
Mutual labels: knowledge-graph, knowledge-base
KGReasoning
Multi-Hop Logical Reasoning in Knowledge Graphs
Stars: ✭ 197 (-48.29%)
Mutual labels: knowledge-graph, knowledge-base
harika
Offline-, mobile-first graph note-taking app focused on performance with the knowledgebase of any scale
Stars: ✭ 111 (-70.87%)
Mutual labels: knowledge-graph, knowledge-base
Tutorial Utilizing Kg
Resources for Tutorial on "Utilizing Knowledge Graphs in Text-centric Information Retrieval"
Stars: ✭ 148 (-61.15%)
Mutual labels: knowledge-graph, knowledge-base
awesome-ontology
A curated list of ontology things
Stars: ✭ 73 (-80.84%)
Mutual labels: knowledge-graph, knowledge-base
Zincbase
A state of the art knowledge base
Stars: ✭ 144 (-62.2%)
Mutual labels: knowledge-graph, knowledge-base
Kbgan
Code for "KBGAN: Adversarial Learning for Knowledge Graph Embeddings" https://arxiv.org/abs/1711.04071
Stars: ✭ 186 (-51.18%)
Mutual labels: knowledge-graph, knowledge-base
Hyte
EMNLP 2018: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Stars: ✭ 130 (-65.88%)
Mutual labels: knowledge-graph, knowledge-base
hugo-documentation-theme
📖 Project Docs / Knowledge Base template for Hugo Website Builder. 创建项目文档
Stars: ✭ 101 (-73.49%)
Mutual labels: knowledge-graph, knowledge-base
Simple
SimplE Embedding for Link Prediction in Knowledge Graphs
Stars: ✭ 104 (-72.7%)
Mutual labels: knowledge-graph, knowledge-base
Workbase
Grakn Workbase (Knowledge IDE)
Stars: ✭ 106 (-72.18%)
Mutual labels: knowledge-graph, knowledge-base
CONVEX
As far as we know, CONVEX is the first unsupervised method for conversational question answering over knowledge graphs. A demo and our benchmark (and more) can be found at
Stars: ✭ 24 (-93.7%)
Mutual labels: knowledge-graph, knowledge-base
typedb
TypeDB: a strongly-typed database
Stars: ✭ 3,152 (+727.3%)
Mutual labels: knowledge-graph, knowledge-base
Dynamic Knowledge Graph Completion
This page is to summarize important materials about dynamic (temporal) knowledge graph completion and dynamic graph embedding.
Bookmarks
- Temporal Knowledge Graph Completion
- Dynamic Graph Embedding
- Knowledge Graph Embedding
- Static Graph Embedding
- Survey
- Others
- Useful Libararies
Temporal Knowledge Graph Completion / Reasoning
-
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
- Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren. EMNLP 2020.
- This work is on an extrapolation problem which is to make predictions at unobserved times, different from interpolation work.
- Proposes a novel neural architecture for modeling complex entity interaction sequences, which consists of a recurrent event encoder and a neighborhood aggregator.
- Explores various neighborhood aggregators: a multi-relational graph aggregator demonstrates its effectiveness among them.
- Code and Data
- Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren. EMNLP 2020.
-
Learning Sequence Encoders for Temporal Knowledge Graph Completion (Interpolation)
- Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.
-
Towards time-aware knowledge graph completion (Interpolation)
- Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016.
-
Deriving validity time in knowledge graph (Interpolation)
- Julien Leblay and Melisachew Wudage Chekol. WWW Workshop 2018.
-
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (Interpolation)
- Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.
- Code (TF based)
-
Predicting the co-evolution of event and knowledge graphs
- Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.
- Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
- Diachronic Embedding for Temporal Knowledge Graph Completion
- Hybrid-TE: Hybrid Translation-based Temporal Knowledge Graph Embedding
- Tensor Decompositions for Temporal Knowledge Base Completion
Dynamic Graph Embedding
-
DyREP: Learning Representations over Dynamic Graphs (Extrapolation)
- Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.
-
DynGEM: Deep Embedding Method for Dynamic Graphs
- Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.
-
Graph2Seq: Scalable Learning Dynamics for Graphs
- Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath
-
Dynamic Graph Representation Learning via Self-Attention Networks
- Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang
-
Continuous-Time Dynamic Network Embeddings
- Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim. WWW 2018.
-
GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction
- Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng
-
Learning Dynamic Embeddings from Temporal Interaction Networks
- Srijan Kumar, Xikun Zhang, Jure Leskovec
-
Dynamic Graph Convolutional Networks
- Franco Manessi, Alessandro Rozza, Mario Manzo
-
Streaming Graph Neural Networks
- Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin
-
Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding
- Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang
-
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
- Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen, ArXiv.
-
Gated Residual Recurrent Graph Neural Networks for Traffic Prediction
- Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng, AAAI 2019.
-
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.
-
Dynamic Network Embedding by Modeling Triadic Closure Process
- Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.
-
DynGAN: Generative Adversarial Networks for Dynamic Network Embedding
- Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal, Ganesh Ramakrishnan. NeurIPS 2019 Workshop.
Knowledge Graph Embedding
-
Modeling Relational Data with Graph Convolutional Networks
- Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.
- Code (Keras based), Code (TF based)
-
Neural Relational Inference for Interacting Systems
- Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.
- Code (Pytorch based)
-
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
- Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari. ICONIP 2017.
Static Graph Embedding
-
Inductive Representation Learning on Large Graphs
- William L. Hamilton, Rex Ying, Jure Leskovec
- Code (TF based), Code (Pytorch based)
-
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
-
Stochastic Training of Graph Convolutional Networks with Variance Reduction
- Jianfei Chen, Jun Zhu, Le Song
-
A Higher-Order Graph Convolutional Layer
- Sami Abu-El-Haija, Nazanin Alipourfard, Hrayr Harutyunyan, Amol Kapoor, Bryan Perozzi
-
Higher-order Graph Convolutional Networks
- John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao
Other Survey Papers
-
Deep Learning on Graphs: A Survey
- Ziwei Zhang, Peng Cui, Wenwu Zhu
-
Graph Neural Networks: A Review of Methods and Applications
- Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun
-
A Comprehensive Survey on Graph Neural Networks
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
-
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
- Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
-
How Powerful are Graph Neural Networks?
- Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.
-
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
- Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart
Others
-
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
- Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager
-
What to Do Next: Modeling User Behaviors by Time-LSTM
- Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.
-
Patient Subtyping via Time-Aware LSTM Networks
- Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.
Useful Libararies
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].