Research Topics in Knowledge Graph

Papers and Materials from All Areas

• Note 1: The papers from the database/data science communities are marked with 🌟.
• Note 2: In Nov 2023, I start to add a new session related to LLM in each topic. 🔥

0. Overview

• Survey of Knowledge Graphs - General Concepts, Scientific Problems, Applications, Challenges, and Directions) [link]

1. Knowledge Extraction and Integration to Construct KGs

• Knowledge Base Construction (Demo or System) [link]

• About Domain-Specific Knowledge Bases [link]

• About Multi-Modal Knowledge Graph (MMKG) [link]

• Named Entity Recognition, Entity Extraction and Entity Typing [link]

• Coreference Resolution [link]

• Entity Linking and Entity Disambiguation [link]

• Entity Resolution, Entity Matching and Entity Alignment [link]

• General Relation Extraction [link]

• General Information Extraction and Open Information Extraction [link]

• Relation Linking and Relation Disambiguation [link]

2. Mining and Refinement of KGs

• Knowledge Graph Embedding, Learning, Reasoning, Rule Mining, and Path Finding [link]

• Knowledge Base Refinement (Incompleteness, Incorrectness, and Freshness) [link]

• Knowledge Fusion, Cleaning, Evaluation and Truth Discovery [link]

3. Applications Supported by KGs

• Knowledge Graph Question Answering (KGQA) [link]

• Knowledge Graph Recommendation [link]

• Knowledge Graph Enhanced Machine Learning [link]

• Knowledge Graphs and Large Language Models (LLMs) [link] 🔥🔥🔥

4. Schema and Query of KGs

• Knowledge Graph Representation (RDF and Property Graph), Schema and Query [link]
• Knowledge Graph Taxonomy Construction and Improvement [link]

5. Others

• Other Interesting Works [link]

• Good DB Papers [link]

Papers and Materials from the Database Communities

Note: Papers from SIGMOD/VLDB/ICDE/KDD/TKDE/VLDBJ

[2018] [2019] [2020] [2021] [2022] [2023] [2024]

Tutorials and Notes from Talented People

Tutorials, Discussion, Courses and Communities

1. An introduction to knowledge graph and knowledge extraction from unstructured text. [Link]
2. Information Extraction by Niranjan Balasubramanian {Slides in my Mac}
3. CS 520 - Knowledge Graphs (seminar) - provided by Stanford
4. OpenKG.cn

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]
2. KG SOTA [GitHub]
3. Awesome KG tutorials/papers/projects/communities [GitHub]
4. Knowledge Graph Construction (from zero to everything, in Chinese) [GitHub]
5. KG SOTA (Chinese) [Zhihu]
6. Tracking Progress in Natural Language Processing [GitHub]
7. KG Embedding SOTA [GitHub]
8. Entity Related Papers [GitHub]
9. Information Extraction Resources [GitHub]
10. KGQA [Giters]
11. Open-Environment Knowledge Graph Construction and Reasoning: Challenges, Approaches, and Opportunities [GitHub]
12. KG-LLM-Papers [Link]
13. Awesome LLM-KGs [Link]

Tutorials and Notes of Other Related Insightful Topics

1. Probabilistic Graphical Models: Lagrangian Relaxation Algorithms for Natural Language Processing [Slides]
2. Introduction to Conditional Random Fields [Blog]
3. Network Community Detection: A Review and Visual Survey [Paper]

• Section 2.3. Community Detection Techniques

4. Fast unfolding of communities in large networks [Paper]

• [A discussion of the Louvain method], [wiki of the Louvein Modularity]
• How do they design the function Q: Finding and evaluating community structure in networks [Paper]

5. A compendium of NP optimization problems [Paper]
6. [Notes about LSH]
7. [Survey about Min Hash Sketch]
8. MinHash Tutorial with Python Code: [Notes] [Code]
9. Must-read papers on GNN [GitHub]
10. Graph-based deap learning literatures [GitHub]
11. Data Management for Machine Learning Applications [Course site]
12. Stanford CS224W: Machine Learning with Graphs [Course site]
13. Explainability for Natural Language Processing (AAAI 2020 tutorial) [Link] [Video]
14. Graph Mining & Learning (Neurips 2020 tutorial) [Link]
15. Discussion about GNN (Chinese) [Link]
16. Stanford CS224n: Natural Language Processing with Deep Learning [Course site]
17. Clique Relaxation Models in Networks: Theory, Algorithms, and Applications [Slides]
18. KG Applications in Baidu (Chinese) [Link]
19. Paper Digest (Database area) [Link]
20. Complex Network (Collection of Notes and Tutorials) [GitHub]
21. INTRODUCTION TO GENERATIVE AI (NTU, Prof HUNG-YI LEE) [Syllabus and Course Materials of Spring 2024]

• A very good and brief overview of Genearative AI! After listening to this course, you are able to clarify the important concepts in generative AI. For example, you can at least tell the difference between prompt engineering and fine-tuning (I think this is a very common mistake made by most people and even papers XD)

Useful Tools or APIs

Named Entity Recogntion and Entity Linking

1. TagMe [Python API] [API] [GitHub1] [GitHub2]
2. Stanford NER [Link]
3. DBpedia Spotlight [Link]
4. NLTK Tagger [Link]
5. SpaCy [Link1] [Link2]

   • spacy-llm [Link]

6. EARL (including Relation Linking) [Link]
7. Falcon (including Relatoin Linking) [DBpedia version] [Wikidata version]
8. MonkeyLearn [Link]
9. GERBIL - General Entity Annotator Benchmark [Link]
10. PIKES [Link]

Benchmark Datasets

1. Entity Disambiguation:

• MSNBC and ACE2004 [Link]

2. QA:

• WebQuestions
• QA datasets summary [GitHub]

3. Entity Resolution [GitHub]
4. KGE, KBC and KG Reasoning

• FB13 [Paper]
• FB15K [Paper]
• FB15K237 [Paper]
• WN11 [Paper]
• WN18 [Paper]
• WN18RR [Paper]

Other Useflul Tools

1. From Freebase to Wikidata: The Great Migration [Paper and useful links]
2. SPARQL tutorial [Link]
3. Installing and running ElasticSearch [Link]
4. Open KG on COVID-19 [Link]
5. BOOKNLP [Link] (Pronominal Coreference Resolution, a natural language processing pipeline that scales to books and other long documents (in English))
6. Wikidata Integrator [GitHub]
7. OpenTapioca [Link]
8. Grakn KGLIB (Knowledge Graph Library) [GitHub]
9. SPASQL server on Freebase [GitHub] [About VOS]
10. LATEX Code Search [Link]