{"id":13408361,"url":"https://github.com/shaoxiongji/knowledge-graphs","last_synced_at":"2025-04-08T01:36:05.456Z","repository":{"id":38411908,"uuid":"163790723","full_name":"shaoxiongji/knowledge-graphs","owner":"shaoxiongji","description":"A collection of research on knowledge graphs","archived":false,"fork":false,"pushed_at":"2022-10-07T16:30:33.000Z","size":204,"stargazers_count":1724,"open_issues_count":0,"forks_count":294,"subscribers_count":62,"default_branch":"master","last_synced_at":"2025-04-01T00:34:00.834Z","etag":null,"topics":["commonsense","cross-modal","dialogue-systems","information-retrieval","knowledge-graph","knowledge-graph-completion","meta-relational-learning","natural-language-processing","ner","paper","question-answering","reasoning","recommendation-systems","relation-extraction","representation-learning","survey","temporal-knowledge-graph"],"latest_commit_sha":null,"homepage":"https://shaoxiongji.github.io/knowledge-graphs/","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shaoxiongji.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-01-02T03:39:25.000Z","updated_at":"2025-03-28T01:40:31.000Z","dependencies_parsed_at":"2022-07-11T03:47:30.554Z","dependency_job_id":null,"html_url":"https://github.com/shaoxiongji/knowledge-graphs","commit_stats":null,"previous_names":["shaoxiongji/awesome-knowledge-graph"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shaoxiongji%2Fknowledge-graphs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shaoxiongji%2Fknowledge-graphs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shaoxiongji%2Fknowledge-graphs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shaoxiongji%2Fknowledge-graphs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shaoxiongji","download_url":"https://codeload.github.com/shaoxiongji/knowledge-graphs/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247423511,"owners_count":20936626,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["commonsense","cross-modal","dialogue-systems","information-retrieval","knowledge-graph","knowledge-graph-completion","meta-relational-learning","natural-language-processing","ner","paper","question-answering","reasoning","recommendation-systems","relation-extraction","representation-learning","survey","temporal-knowledge-graph"],"created_at":"2024-07-30T20:00:52.382Z","updated_at":"2025-04-08T01:36:05.434Z","avatar_url":"https://github.com/shaoxiongji.png","language":"JavaScript","readme":"# Knowledge Graphs\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n[![GitHub issues](https://img.shields.io/github/issues/shaoxiongji/knowledge-graphs)](https://github.com/shaoxiongji/knowledge-graphs/issues)\n[![GitHub forks](https://img.shields.io/github/forks/shaoxiongji/knowledge-graphs)](https://github.com/shaoxiongji/knowledge-graphs/network)\n[![GitHub stars](https://img.shields.io/github/stars/shaoxiongji/knowledge-graphs)](https://github.com/shaoxiongji/knowledge-graphs/stargazers)\n[![Twitter](https://img.shields.io/twitter/url?style=social)](https://twitter.com/intent/tweet?text=Wow:\u0026url=https%3A%2F%2Fgithub.com%2Fshaoxiongji%2Fknowledge-graphs)\n\nA collection of knowledge graph papers, codes, and reading notes.\n\n- [Knowledge Graphs](#knowledge-graphs)\n  - [Survey](#survey)\n  - [Papers by venues](#papers-by-venues)\n  - [Papers by categories](#papers-by-categories)\n  - [Data](#data)\n    - [General Knowledge Graphs](#general-knowledge-graphs)\n    - [Domain-specific Data](#domain-specific-data)\n    - [Entity Recognition](#entity-recognition)\n    - [Other Collections](#other-collections)\n  - [Libraries, Softwares and Tools](#libraries-softwares-and-tools)\n    - [KRL Libraries](#krl-libraries)\n    - [Knowledge Graph Database](#knowledge-graph-database)\n    - [Others](#others)\n    - [Interactive APP](#interactive-app)\n  - [Courses, Tutorials and Seminars](#courses-tutorials-and-seminars)\n    - [Courses](#courses)\n  - [Related Repos](#related-repos)\n  - [Acknowledgements](#acknowledgements)\n\n\n## Survey\n__A Survey on Knowledge Graphs: Representation, Acquisition and Applications__. IEEE TNNLS 2021. _Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu_. [[Paper](https://arxiv.org/pdf/2002.00388)] \n\n__Knowledge Graphs__. Preprint 2020. _Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann_. [[Paper](https://arxiv.org/abs/2003.02320)] \n\n__Knowledge Representation Learning: A Quantitative Review__. Preprint 2018. _Lin, Yankai and Han, Xu and Xie, Ruobing and Liu, Zhiyuan and Sun, Maosong_. [[Paper](https://arxiv.org/pdf/1812.10901)]\n\n__Knowledge graph embedding: A survey of approaches and applications__. TKDE 2017. _Wang, Quan and Mao, Zhendong and Wang, Bin and Guo, Li_. [[Paper](https://persagen.com/files/misc/Wang2017Knowledge.pdf)]\n\n__Knowledge graph refinement: A survey of approaches and evaluation methods__. Semantic Web 2017. _Paulheim, Heiko_. [[Paper](http://www.semantic-web-journal.net/system/files/swj1167.pdf)]\n\n__A review of relational machine learning for knowledge graphs__. Proceedings of the IEEE 2015. _Nickel, Maximilian and Murphy, Kevin and Tresp, Volker and Gabrilovich, Evgeniy_. [[Paper](https://arxiv.org/pdf/1503.00759)]\n\n## Papers by venues\n| Year      | WWW                           | AAAI                            |  ACL  | \n| --------  | --------                      | --------                        |  --------                        |              \n| 2020      | [20](./conferences/www20.md)  | [28](./conferences/aaai20.md)   |  [53](./conferences/acl20.md)    |\n\n## Papers by categories\n- [Knowledge Graph Embedding](./papers/KG-embedding.md)\n- [Cross-Modal KG Embedding](./papers/KG-cross-modal.md)\n- Knowledge Acquisition\n  - [Knowledge Graph Completion](./papers/KG-KGC.md)\n  - [Relation Extraction](./papers/KG-RE.md)\n  - [Entity Discovery](./papers/KG-entity.md)\n- Knowledge-aware Applications\n  - [Natural Language Understanding](./papers/KG-applications.md#natural-langauge-understanding)\n  - [Commonsense Knowledge](./papers/KG-applications.md#commonsense-knowledge)\n  - [Question Answering](./papers/KG-applications.md#question-answering)\n  - [Dialogue Systems](./papers/KG-applications.md#dialogue-systems)\n  - [Recommendation Systems](./papers/KG-applications.md#recommendation-systems)\n  - [Information Retrieval](./papers/KG-applications.md#information-retrieval)\n- [Temporal Knowledge Graph](./papers/KG-temporal.md)\n- [Knowledge Graph Reasoning](./papers/KG-reasoning.md)\n- [One/few-Shot and Zero-Shot Learning](./papers/KG-few-shot.md)\n- [Domain-specific Knowledge Graphs](./papers/KG-domain.md)\n- [KG Database Systems](./papers/KG-database.md)\n\n## Data\n### General Knowledge Graphs\n- WordNet, https://wordnet.princeton.edu\n- OpenCyc, https://www.cyc.com/opencyc/\n- Cyc, https://www.cyc.com\n- YAGO, http://www.mpii.mpg.de/∼suchanek/yago\n- DBpedia, https://wiki.dbpedia.org/develop/datasets\n- Freebase, https://developers.google.com/freebase/\n- NELL, http://rtw.ml.cmu.edu/rtw/\n- Wikidata, https://www.wikidata.org/wiki\n- Probase IsA, https://concept.research.microsoft.com/Home/Download\n- Google KG, https://developers.google.com/knowledge-graph\n- A large-scale Chinese knowledge graph from [OwnThink](https://github.com/ownthink/KnowledgeGraph)\n- GDELT（Global Database of Events, Language, and Tone）[Web](https://www.gdeltproject.org)\n- [KGHUB and KGOBO, Biomedical ontologies](https://kg-hub.berkeleybop.io/)\n- [PheKnowLator: Heterogeneous Biomedical Knowledge Graphs and Benchmarks Constructed Under Alternative Semantic Models](https://github.com/callahantiff/PheKnowLator)\n\n### Domain-specific Data\n__OpenKG knowledge graphs about the novel coronavirus COVID-19__\n- 新冠百科图谱 [[链接](http://www.openkg.cn/dataset/2019-ncov-baike)] Knowledge graph from encyclopedia[[Link](http://www.openkg.cn/dataset/2019-ncov-baike)]\n\n- 新冠科研图谱 [[链接](http://www.openkg.cn/dataset/2019-ncov-research)] Knowledge graph of COVID-19 research [[Link](http://www.openkg.cn/dataset/2019-ncov-research)]\n\n- 新冠临床图谱 [[链接](http://www.openkg.cn/dataset/2019-ncov-clinic)] Clinical knowledge graph [[Link](http://www.openkg.cn/dataset/2019-ncov-clinic)]\n\n- 新冠英雄图谱 [[链接](http://www.openkg.cn/dataset/2019-ncov-hero)] Knowledge graph of people, experts, and heroes [[Link](http://www.openkg.cn/dataset/2019-ncov-hero)]\n\n- 新冠热点事件图谱 [[链接](http://www.openkg.cn/dataset/2019-ncov-event)] Knowledge graph of public events [[Link](http://www.openkg.cn/dataset/2019-ncov-event)]\n\n__COVID❋GRAPH  COVID-19 virus__\n[[Web](http://www.odbms.org/2020/03/we-build-a-knowledge-graph-on-covid-19/)]\n\n__KgBase COVID-19 knowledge graph__ [[Web](https://covid19.kgbase.com)]\n__Academic graphs__\n- OAG, Open Academic Graph, https://www.aminer.cn/open-academic-graph\n\n### Entity Recognition\nCORD-19, a comprehensieve named entity annotation dataset, CORD-NER, on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus [[Data](https://xuanwang91.github.io/2020-03-20-cord19-ner/)]\n\n### Other Collections\nBaidu BROAD datasets [[Web](https://ai.baidu.com/broad/introduction)]\n\n__ASER: A Large-scale Eventuality Knowledge Graph__\nWWW 2020. _Zhang et al._ [[Paper](https://dl.acm.org/doi/abs/10.1145/3366423.3380107)]\n\n\n## Libraries, Softwares and Tools\n### KRL Libraries\n\n* [TypeDB](https://github.com/vaticle/kglib), TypeDB Knowledge Graph Library (ML R\u0026D) https://www.vaticle.com\n* [AmpliGraph](https://github.com/Accenture/AmpliGraph), Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org\n* [OpenKE](https://github.com/thunlp/OpenKE), An Open-Source Package for Knowledge Embedding (KE)\n* [Fast-TransX](https://github.com/thunlp/Fast-TransX), An Efficient implementation of TransE and its extended models for Knowledge Representation Learning\n* [scikit-kge](https://github.com/mnick/scikit-kge), Python library to compute knowledge graph embeddings\n* [OpenNRE](https://github.com/thunlp/OpenNRE), An Open-Source Package for Neural Relation Extraction (NRE)\n* [PyKEEN](https://github.com/pykeen/pykeen), 🤖 A Python library for learning and evaluating knowledge graph embeddings\n* [🍇 GRAPE](https://github.com/AnacletoLAB/grape), A Rust/Python library for Graph Representation Learning, Predictions and Evaluations\n\n### Knowledge Graph Database\n[akutan](https://github.com/eBay/akutan), A distributed knowledge graph store\n\n### Others\n- [OpenNRE](https://github.com/thunlp/OpenNRE)\n\n### Interactive APP\nKnowledge graph APP, Simple knowledge graph applications can be easily built using JSON data managed entirely via a GraphQL layer. [[Github](https://github.com/epistemik-co/staple-api-kg-demo)] [[Website](http://demo.staple-api.org)]\n\n## Courses, Tutorials and Seminars\n### Courses\n- Stanford CS 520 Knowledge Graphs: How should AI explicitly represent knowledge? _Vinay K. Chaudhri, Naren Chittar, Michael Genesereth_. [[Web](https://web.stanford.edu/class/cs520/)]\n- Stanford CS 224W: Machine Learning with Graphs. _Jure Leskovec_. [[Web](http://web.stanford.edu/class/cs224w/index.html)]\n- University of Bonn: Analysis of Knowledge Graphs. _Jens Lehmmann_. [[Web](https://sewiki.iai.uni-bonn.de/teaching/lectures/kga/2017/start)] [[GitHub](https://github.com/SmartDataAnalytics/Knowledge-Graph-Analysis-Programming-Exercises)]\n- Knowledge Graphs. _Harald Sack, Mehwish Alam_. [[Web](https://open.hpi.de/courses/knowledgegraphs2020)]\n\n\n## Related Repos\nA repo about knowledge graph in Chinese - [husthuke/awesome-knowledge-graph](https://github.com/husthuke/awesome-knowledge-graph)\n\nA repo about NLP, KG, Dialogue Systems in Chinese - [lihanghang/NLP-Knowledge-Graph](https://github.com/lihanghang/NLP-Knowledge-Graph)\n\nTop-level Conference Publications on Knowledge Graph - [wds-seu/Knowledge-Graph-Publications](https://github.com/wds-seu/Knowledge-Graph-Publications)\n\nGeospatial Knowledge Graphs - [semantic-geospatial](https://github.com/laurentlefort/semantic-geospatial/wiki/Geospatial-Knowledge-Graphs)\n\n## Acknowledgements\n\nAcknowledgments give to the following people who comment or contribute to this repository (listed chronologically).\n\n- [DonaldTsang](https://github.com/DonaldTsang)\n- [NYXFLOWER](https://github.com/NYXFLOWER)\n- [Arun-George-Zachariah](https://github.com/Arun-George-Zachariah)  \n\n__[⬆](#awesome-knowledge-graph)__\n","funding_links":[],"categories":["Machine Learning (ML) and Data Mining (DM)","JavaScript","Table of Contents","Uncategorized","📦 Legacy \u0026 Inactive Projects","others","natural-language-processing","Other Lists","Tutorials and Notes from Talented People"],"sub_categories":["Uncategorized","TeX Lists","5. 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