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https://github.com/BrambleXu/knowledge-graph-learning

A curated list of awesome knowledge graph tutorials, projects and communities.
https://github.com/BrambleXu/knowledge-graph-learning

knowledge-graph knowledge-graph-embeddings relation-extraction

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A curated list of awesome knowledge graph tutorials, projects and communities.

Awesome Lists containing this project

README 

        
# awesome-knowledge-graph[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



A curated list of awesome knowledge graph tutorials, projects and communities.

Both Chinese and English resource are listed in language respectively.

Please feel free to pull requests to add links.



## Table of Contents



* **[Papers](#papers)**

* **[Useful Articles/Slides](#useful-articlesslides)**

* **[Courses and Lectures](#courses-and-lectures)**

* **[Datasets](#datasets)**

* **[Implementations and Tools](#implementations-and-tools)**

* **[Community](#community)**



## Papers



I write notes of paper and post them in the issue. It is written in Chinese. Feel free to post your notes no matter what language you use.



**Knowledge Graph Related task**



- [Information Extraction/Open IE](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3AIE%28T%29+)

- [Knowledge Graph Population/Construction Task](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3AKGP%2FKGC%28T%29+): Construct a Knowledge Graph from Different Sources

- [Knowledge Base Completion/Knowledge Graph Reasoning](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=is%3Aissue+is%3Aopen+label%3AKBC%2FKGR%28T%29): Entity Prediction or Link Prediction

- [Knowledge Representation Learning & Knowledge Embedding](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=%E2%9C%93&q=label%3AKRL%2FKGE%28%28T%2FM%29+)

- [Knowledge based Recommendation System](https://github.com/BrambleXu/knowledge-graph-learning/issues/250)

- [Named Entity Linking](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3ANEL%28T%29+)

- [Named Entity Recognition](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ANER%28T%29+)

- [Ontology-based information extraction](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3AOBIE%28T%29+)

- [Relation Extraction](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ARE%28T%29+)

- [Semantic Role Labeling](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ASRL%28T%29+)



other non-related task paper




**Tag with task**



- [Annotation](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3AAnnotation%28T%29+)

- [Coreference Resolution](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3ACR%28T%29+)

- [Data Augmentation](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=is%3Aopen+is%3Aissue+label%3ADataAug%28T%29)

- [Dependency Parsing](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ADP%28T%29+)

- [Domain Adaptation/Domain Specific](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ADA%28T%29+)

- [Natural Language Understanding](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3ANLU%28T%29+)

- [Neural Machine Translation](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3ANMT%28T%29+)

- [Question Answering/Machine Comprehension](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3AQA%28T%29+)

- [Recommendation](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=is%3Aopen+is%3Aissue+label%3ARecommendation%28T%29)

- [Relational Reasoning](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=label%3ARR%28T%29)

- [Summarization](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=is%3Aopen+is%3Aissue+label%3ASummarization%28T%29)

- [Slot Filling](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3ASF%28T%29+)

- [Text Classification](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=label%3ATC%28T%29)



**Tag with Model**



- [BERT](https://github.com/BrambleXu/knowledge-graph-learning/issues?q=label%3ABERT%28M%29)

- [Embedding/Pre-train Model/Task](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3AEmbedding+)

- [End-to-end Model](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3AE2E%28M%29+)

- [Graph Neural Network](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=label%3AGNN%28M%29+)

- [Multi-Task/Joint Learning](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3AMTL%28M%29+)

- [Transformer Based Model](https://github.com/BrambleXu/knowledge-graph-learning/issues?utf8=✓&q=+label%3ATransformer%28M%29+)






## Useful Articles/Slides



* [ontotext blog](https://www.ontotext.com/category/business/)

* [Knowledge Extraction and Inference from Text (KDD 2018 Tutorial)](https://sites.google.com/site/keit2018kdd/)

* [Enterprise	Knowledge	Graphs for Large	Scale	Analytics](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial1-1.pdf) from IBM

* [Getting Started with Knowledge Graphs](https://www.slideshare.net/phaase/getting-started-with-knowledge-graphs) from metaphacts

* [Mining Knowledge Graphs from Text: A Tutorial](https://kgtutorial.github.io) from WSDM 2018 Tutorial

* [Knowledge Graphs - The Power of Graph-Based Search](https://www.slideshare.net/neo4j/knowledge-graphs-the-power-of-graphbased-search) from Neo4j

* [Knowledge Integration in Practice](https://www.slideshare.net/pmika/knowledge-integration-in-practice) from YAHOO

* [知识图谱论文合集](https://zhuanlan.zhihu.com/p/44904796)

* [知识图谱入门 (三)](http://pelhans.com/2018/03/19/xiaoxiangkg-note3/#%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96)

* [知识图谱上的实体链接](http://blog.openkg.cn/%e6%8a%80%e6%9c%af%e5%8a%a8%e6%80%81-%e7%9f%a5%e8%af%86%e5%9b%be%e8%b0%b1%e4%b8%8a%e7%9a%84%e5%ae%9e%e4%bd%93%e9%93%be%e6%8e%a5/)

* [Recent trends of Entiy Linking](https://github.com/izuna385/EntityLinking_RecentTrend)



### Relation Extraction



* [A SURVEY ON RELATION EXTRACTION (CMU)](http://www.cs.cmu.edu/~nbach/papers/A-survey-on-Relation-Extraction-Slides.pdf)

* [Relation Extraction: CSE 517: Natural Language Processing](https://courses.cs.washington.edu/courses/cse517/13wi/slides/cse517wi13-RelationExtraction.pdf)

* [Relation Extraction II: CSE 517: Natural Language Processing](https://courses.cs.washington.edu/courses/cse517/13wi/slides/cse517wi13-RelationExtractionII.pdf)



### Event Extraction



* [事件抽取与金融事件图谱构建](https://www.jiqizhixin.com/articles/2018-10-17-12)



### Survey

* A Survey on Knowledge Graphs: Representation, Acquisition and Applications (2020). Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu. [[Paper]](https://arxiv.org/pdf/2002.00388) 

* Deep Learning in Knowledge Graph (2018), [[Note]](https://github.com/BrambleXu/knowledge-graph-learning/issues/31)

* 知识图谱研究进展 (2017), 漆桂林等人. [[PDF]](http://tie.istic.ac.cn/ch/reader/create_pdf.aspx?file_no=201701002&flag=&journal_id=qbgc&year_id=2017)

* 知识图谱构建技术综述 (2016), 刘峤等人. [[PDF]](http://crad.ict.ac.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=3127)

* 知识图谱技术综述 (2016), 徐增林等人. [[PDF]](http://www.xml-data.org/dzkj-nature/html/201645589.htm)

* 知识图谱：大数据语义链接的基石 (2014), 李涓子. [[PPT]](http://bj.bcebos.com/cips-upload/kg2/kg2_ljz.pdf)

* 垂直知识图谱构造工具与行业应用 (2014), 阮彤. [[PPT]](http://bj.bcebos.com/cips-upload/kg2/kg2_rt.pdf)

* [Summary of Translate Model for Knowledge Graph Embedding](https://medium.com/@zhuixiyou/summary-of-translate-model-for-knowledge-graph-embedding-29042be64273)



## Courses and Lectures



* [从零开始构建知识图谱（知乎专栏）](https://zhuanlan.zhihu.com/c_1018901137012928512)



## Datasets



### Relation Extraction Dataset



- [SemEval-2010 Task 8](https://github.com/sahitya0000/Relation-Classification), [link 2](https://github.com/shashwath94/Relation-Extraction-using-CNN)

- [TACRED(charge)](https://nlp.stanford.edu/projects/tacred/)

- [KGHUB and KGOBO, Biomedical ontologies](https://kg-hub.berkeleybop.io/)

- [PheKnowLator: Heterogeneous Biomedical Knowledge Graphs and Benchmarks Constructed Under Alternative Semantic Models](https://github.com/callahantiff/PheKnowLator)



### Open datasets



* [Annotated-Semantic-Relationships-Datasets(English)](https://github.com/davidsbatista/Annotated-Semantic-Relationships-Datasets)

* [OpenKG.CN Datasets List(Chinese)](http://openkg.cn/dataset)

* [Zhishi.me(Chinese)](http://zhishi.me/)



## Implementations and Tools



### Implementations



* [Knowledge Graph Demo (上市公司高管图谱)](https://github.com/Shuang0420/knowledge_graph_demo), [文章](http://www.shuang0420.com/2017/09/05/%E9%A1%B9%E7%9B%AE%E5%AE%9E%E6%88%98-%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1%E5%88%9D%E6%8E%A2/)

* [KGQA_HLM (红楼梦 人物关系可视化及问答系统)](https://github.com/chizhu/KGQA_HLM)

* [从零开始搭建一个电影知识图谱](https://github.com/Pelhans/Z_knowledge_graph)

* [基于elasticsearch的KBQA实现及示例](http://www.openkg.cn/tool/elasticsearch-kbqa)

* [电影知识图谱以及KBQA实现](https://github.com/SimmerChan/KG-demo-for-movie), [知乎文章](https://zhuanlan.zhihu.com/p/33363861)



### Tools



* [[Resource] Useful tools & lecture related to data science(中文)](https://github.com/BrambleXu/knowledge-graph-learning/issues/131)

* [InteractiveGraph](https://github.com/grapheco/InteractiveGraph): [中文介绍](https://blog.csdn.net/bluejoe2000/article/details/104333111)

* [Annotation tool: doccano](https://github.com/chakki-works/doccano)

* [OpenNRE: An Open-Source Package for Neural Relation Extraction (NRE) implemented in TensorFlow](https://github.com/thunlp/OpenNRE/), [NER paper](https://github.com/thunlp/NREPapers)

* [DeepDive: a system to extract value from dark data](https://github.com/HazyResearch/deepdive), [Homepage](http://deepdive.stanford.edu/), [Papers](https://github.com/HazyResearch/deepdive/blob/master/doc/papers.md)

* [HanLP: Han Language Processing（汉语言处理包）](https://github.com/hankcs/HanLP)

* [句法依存分析抽取事实三元组](https://github.com/twjiang/fact_triple_extraction)

* [cnSchema - 开放的中文知识图谱schema](https://github.com/cnschema/cnschema)

* [知识图谱API](https://github.com/ownthink/KnowledgeGraph)

* [OpenKE: An Open-source Framework for Knowledge Embedding](https://github.com/thunlp/OpenKE)

* [Zhishi.me: Chinese Linking Open Data Online API](http://zhishi.me/)

* [PyKEEN](https://github.com/pykeen/pykeen), 🤖 A Python library for learning and evaluating knowledge graph embeddings

* [🍇 GRAPE](https://github.com/AnacletoLAB/grape), A Rust/Python library for Graph Representation Learning, Predictions and Evaluations



## Community



* [OpenKG.CN (开放的中文知识图谱)](http://openkg.cn/)

* [北京知识图谱学习小组](https://github.com/memect/kg-beijing)