# Knowledge-Graph-Tutorials-and-Papers

**Repository Path**: uisu/Knowledge-Graph-Tutorials-and-Papers

## Basic Information

- **Project Name**: Knowledge-Graph-Tutorials-and-Papers
- **Description**: 关于知识图谱的系列文章介绍，导自：https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers
为啥导到gitee上，github太慢了
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-11
- **Last Updated**: 2021-12-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Papers and Materials from All Areas
======
Note: The papers in database area (or written in database style) are marked with 🌟 

### Knowledge Extraction and Integration to Construct KGs
* __Knowledge Base Construction (Demo or System) [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Base%20Construction%20(Demo%20or%20System).md)__

* __About Domain-Specific Knowledge Bases [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/About%20Domain-Specific%20Knowledge%20Bases.md)__

* __Named Entity Recoginition, Entity Extraction and Entity Typing [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Named%20Entity%20Recoginition%2C%20Entity%20Extraction%20and%20Entity%20Typing.md)__

* __Coreference Resolution [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Coreference%20Resolution.md)__

* __Entity Linking and Entity Disambiguation [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Entity%20Linking%20and%20Entity%20Disambiguation.md)__

* __Entity Resolution, Entity Matching and Entity Alignment [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Entity%20Resolution%2C%20Entity%20Matching%20and%20Entity%20Alignment.md)__

* __General Relation Extraction [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Relation%20Extraction.md)__

* __General Information Extraction and Open Information Extraction [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Open%20Information%20Extraction.md)__

* __Relation Linking and Relation Disambiguation [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Relation%20Linking%20and%20Relation%20Disambiguation.md)__

### Mining and Refinement of KGs
* __Knowledge Graph Embedding, Learning, Reasoning, Rule Mining, and Path Finding [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Graph%20Embedding%2C%20Learning%2C%20Reasoning%2C%20Rule%20Mining%2C%20and%20Path%20Finding.md)__

* __Knowledge Base Refinement (Incompleteness, Incorrectness, and Freshness) [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Base%20Refinement%20(Incompleteness%2C%20Incorrectness%2C%20and%20Freshness).md)__

* __Knowledge Fusion, Cleaning, Evaluation and Truth Discovery [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Fusion%2C%20Cleaning%2C%20Evaluation%20and%20Truth%20Discovery.md)__

### Applications Supported by KGs
* __Knowledge Graph Question Answering (KGQA) [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Graph%20Question%20Answering%20(KGQA).md)__

* __Knowledge Graph Recommendation [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Graph%20Recommendation.md)__

* __Knowledge Graph Enhanced Machine Learning [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Graph%20Enhanced%20Machine%20Learning.md)__

### Schema and Query of KGs
* __Knowledg Graph Representation (RDF and Property Graph), Schema and Query [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledg%20Graph%20Representation%20(RDF%20and%20Property%20Graph)%20and%20Schema.md)__

### Others
* __Other Interesting Works [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Other%20Interesting%20Works.md)__

* __Good DB Papers [[link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Good%20DB%20papers.md)__

Papers and Materials from Database Area
======
Note: Papers from SIGMOD/VLDB/ICDE/KDD/TKDE/VLDBJ

#### 2018 [[Link]](https://github.com/heathersherry/Knowledge-Graphs-and-Data-Integration-in-Database-Conferences-2018)
#### 2019 [[Link]](https://github.com/heathersherry/Knowledge-Graphs-and-Data-Integration-in-Database-Conferences-2019)
#### 2020 [[Link]](https://github.com/heathersherry/Knowledge-Graphs-and-Data-Integration-in-Database-Conferences-2020-)
#### 2021 [[Link]](https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/DB-2021.md)

Tutorials and Notes from Talented People
=====
#### Tutorials, Discussion and Communities
1. An introduction to knowledge graph and knowledge extraction from unstructured text. [[Link](https://kgtutorial.github.io)]
2. Information Extraction by Niranjan Balasubramanian {Slides in my Mac}
3. [CS 520 - Knowledge Graphs (seminar) - provided by Stanford](https://web.stanford.edu/class/cs520/)
4. [OpenKG.cn](http://www.openkg.cn/home)

#### GitHub Repos that Summarize the Papers/Projects/Data related to Knowledge Graphs
1. A Collection of KG Surveys, Papers (WWW+ACL+AAAI) and Data [[GitHub](https://github.com/shaoxiongji/knowledge-graphs#survey)]
2. KG STOA [[GitHub](https://github.com/impillar/knowledge_graph/blob/master/README.md)]
3. Awesome KG tutorials/papers/projects/communities [[GitHub](https://github.com/BrambleXu/knowledge-graph-learning)]
4. Knowledge Graph Construction (from zero to everything, in Chinese) [[GitHub](https://github.com/Pelhans/Z_knowledge_graph)]
5. KG STOA (Chinese) [[Zhihu](https://zhuanlan.zhihu.com/p/44904796)]
6. Tracking Progress in Natural Language Processing [[GitHub](https://github.com/sebastianruder/NLP-progress)]
7. KG Embedding STOA [[GitHub](https://github.com/xinguoxia/KGE)]
8. Entity Related Papers [[GitHub](https://github.com/HelloRusk/entity-related-papers)]
9. Information Extraction Resources [[GitHub](https://github.com/casnlu/InformationExtraction)]


#### Tutorials and Notes of Other Related Insightful Topics
1. Probabilistic Graphical Models: Lagrangian Relaxation Algorithms for Natural Language Processing [[Slides](http://people.csail.mit.edu/dsontag/courses/pgm12/slides/lecture3.pdf)]
2. Introduction to Conditional Random Fields [[Blog](http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/)]
3. Network Community Detection: A Review and Visual Survey [[Paper](https://arxiv.org/pdf/1708.00977.pdf)]
> * Section 2.3. Community Detection Techniques 
4. Fast unfolding of communities in large networks [[Paper](https://arxiv.org/pdf/0803.0476.pdf)]
> * [[A discussion of the Louvain method](https://www.quora.com/Is-there-a-simple-explanation-of-the-Louvain-Method-of-community-detection)], [[wiki of the Louvein Modularity](https://en.wikipedia.org/wiki/Louvain_Modularity)]
> * How do they design the function Q: Finding and evaluating community structure in networks [[Paper](https://arxiv.org/abs/cond-mat/0308217)]
5. A compendium of NP optimization problems [[Paper](https://www.semanticscholar.org/paper/A-compendium-of-NP-optimization-problems-Crescenzi-Kann/d5a16ac8dd6781090292b7db0a21e4240ffe56b0)]
6. [[Notes about LSH](https://blog.csdn.net/yc461515457/article/details/48845775)]
7. [[Survey about Min Hash Sketch](http://www.cohenwang.com/edith/Surveys/minhash.pdf)]
8. MinHash Tutorial with Python Code: [[Notes](https://mccormickml.com/2015/06/12/minhash-tutorial-with-python-code/)] [[Code](https://github.com/chrisjmccormick/MinHash)]
9. Must-read papers on GNN [[GitHub](https://github.com/thunlp/GNNPapers)]
10. Graph-based deap learning literatures [[GitHub](https://github.com/naganandy/graph-based-deep-learning-literature)]
11. Data Management for Machine Learning Applications [[Course site](https://thodrek.github.io/CS839_spring18/)]
12. Stanford CS224W: Machine Learning with Graphs [[Course site](http://web.stanford.edu/class/cs224w/)]
13. Explainability for Natural Language Processing (AAAI 2020 tutorial) [[Link](http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing)] [[Video](https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be)]
14. Graph Mining & Learning (Neurips 2020 tutorial) [[Link](https://gm-neurips-2020.github.io)]
15. Discussion about GNN (Chinese) [[Link](https://developer.aliyun.com/article/741923?utm_content=g_1000099113)]
16. Stanford CS224n: Natural Language Processing with Deep Learning [[Course site](http://web.stanford.edu/class/cs224n/)]
17. Clique Relaxation Models in Networks: Theory, Algorithms, and Applications [[Slides](https://www.slideshare.net/ssakpi/clique-relaxation-models-in-networks-theory-algorithms-and-applications)]
18. KG Applications in Baidu (Chinese) [[Link](https://mp.weixin.qq.com/s/z3cp4PaAsA2zGLlgfYAtTg)]
19. Paper Digest (Database area) [[Link](https://www.paperdigest.org/category/database/)]
20. Complex Network (Collection of Notes and Tutorials) [[GitHub](https://github.com/LiuChuang0059/Complex-Network)]

Useful Tools or APIs
====
#### Named Entity Recogntion and Entity Linking
1. TagMe [[Python API](https://pypi.org/project/tagme/)] [[API](https://tagme.d4science.org/tagme/)] [[GitHub1](https://github.com/marcocor/tagme-python)] [[GitHub2](https://github.com/gammaliu/tagme)]
2. Stanford NER [[Link](https://nlp.stanford.edu/software/CRF-NER.html)]
3. DBpedia Spotlight [[Link](https://www.dbpedia-spotlight.org/)]
4. NLTK Tagger [[Link](https://www.nltk.org/book/ch05.html)]
5. SpaCy [[Link1](https://spacy.io/api/annotation#section-named-entities)] [[Link2](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da)]
6. EARL (including Relation Linking) [[Link](https://github.com/AskNowQA/EARL)]
7. Falcon (including Relatoin Linking) [[DBpedia version](https://github.com/AhmadSakor/falcon)] [[Wikidata version](https://github.com/SDM-TIB/falcon2.0)] 
8. MonkeyLearn [[Link](https://monkeylearn.com/blog/named-entity-recognition-python/)]
9. GERBIL - General Entity Annotator Benchmark [[Link](http://gerbil.aksw.org/gerbil/)]
10. PIKES [[Link](http://pikes.fbk.eu)]

#### Benchmark Datasets
1. Entity Disambiguation: 
* MSNBC and ACE2004 [[Link](https://users.dcc.uchile.cl/~hrosales/MSNBC_ACE2004_to_NIF.html)]
2. QA: 
* WebQuestions
* QA datasets summary [[GitHub](https://github.com/sebastianruder/NLP-progress/blob/master/english/question_answering.md)]
3. Entity Resolution [[GitHub](https://github.com/scify/JedAIToolkit/tree/master/data)]
4. KGE, KBC and KG Reasoning
* FB13 [[Paper](https://proceedings.neurips.cc/paper/2013/file/b337e84de8752b27eda3a12363109e80-Paper.pdf)]
* FB15K [[Paper](https://proceedings.neurips.cc/paper/2013/file/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf)]
* FB15K237 [[Paper](https://aclanthology.org/D15-1174/)]
* WN11 [[Paper](https://proceedings.neurips.cc/paper/2013/file/b337e84de8752b27eda3a12363109e80-Paper.pdf)]
* WN18 [[Paper](https://proceedings.neurips.cc/paper/2013/file/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf)]
* WN18RR [[Paper](https://arxiv.org/abs/1707.01476)]
 
#### Other Useflul Tools
1. From Freebase to Wikidata: The Great Migration [[Paper and useful links](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44818.pdf)] 
2. SPASQL tutorial [[Link](https://www.w3.org/2009/Talks/0615-qbe/)]
3. Installing and running ElasticSearch [[Link](https://www.elastic.co/guide/en/elasticsearch/reference/current/targz.html)]
4. Open KG on COVID-19 [[Link](http://openkg.cn/dataset/covid-19-research)]
5. BOOKNLP [[Link](https://github.com/dbamman/book-nlp)] (Pronominal Coreference Resolution, a natural language processing pipeline that scales to books and other long documents (in English))
6. Wikidata Integrator [[GitHub](https://github.com/SuLab/WikidataIntegrator)]
7. OpenTapioca [[Link](https://opentapioca.readthedocs.io/en/latest/install.html)]
8. Grakn KGLIB (Knowledge Graph Library) [[GitHub](https://github.com/graknlabs/kglib)]
9. SPASQL server on Freebase [[GitHub](https://github.com/xwhan/Freebase-SPARQL-server-on-AWS)] [[About VOS](http://vos.openlinksw.com/owiki/wiki/VOS)]
10. LATEX Code Search [[Link](https://www.latex4technics.com/codesearch)]