Awesome Knowledge Graph Resources: Papers, Tools, Datasets, and Projects

The "awesome" series on GitHub gathers high‑quality resources for various fields; this article introduces several awesome‑knowledge‑graph collections that aggregate papers, tools, datasets, and tutorials.

1. Awesome Knowledge Graph

Two repositories are highlighted:

https://github.com/husthuke/awesome-knowledge-graph – a comprehensive list of KG papers, pretrained language models, datasets, and tools, organized into sections such as papers, datasets, tools, reports, institutions, videos, columns, benchmarks, projects, and articles.

https://github.com/totogo/awesome-knowledge-graph – an English‑language collection focusing on practical KG tools and datasets, including graph databases (Neo4j, OrientDB), triple stores (Jena), graph computation frameworks (Spark), visualization (AntV G6), query languages (Cypher, SPARQL), knowledge fusion tools, and popular KG datasets (Wikidata, DBpedia, YAGO, ConceptNet, WordNet, Aminer).

2. Awesome Relation Extraction

https://github.com/roomylee/awesome-relation-extraction aggregates top‑conference papers and methods for relation extraction, covering supervised approaches (CNN, RNN, GNN, dependency‑based), distant supervision, language‑model based methods, KG‑enhanced techniques, and few‑shot learning, along with typical datasets and open‑source tools.

3. Awesome KG‑QA

https://github.com/BshoterJ/awesome-kgqa summarizes knowledge‑base question answering (KBQA) research, categorizing papers into text‑matching and semantic parsing methods, and lists high‑star open‑source projects useful for building simple KBQA systems.

4. Awesome Knowledge Graph Construction

https://github.com/songjiang0909/awesome-knowledge-graph-construction reviews construction‑oriented resources, describing manual (expert, crowdsourced) and automated methods (semi‑structured, unstructured, OpenIE), and mentions tools such as DeepDive and OpenIE.

5. Awesome Graph Neural Networks

https://github.com/GRAND-Lab/Awesome-Graph-Neural-Networks provides an extensive list of GNN papers, surveys, method categories (recurrent, spectral, spatial, auto‑encoders, spatio‑temporal) and applications across AI domains (vision, NLP, recommendation, medicine, chemistry, physics).

It also links to a GNN‑based recommendation collection ( https://github.com/Jhy1993/Awesome-GNN-Recommendation ) that includes papers, code, datasets, tutorials, and community resources.

6. Awesome Dynamic Graph Learning

https://github.com/SpaceLearner/Awesome-DynamicGraphLearning focuses on dynamic/temporal graphs and knowledge graphs, listing recent papers, code, and tools, and highlights 2021 works on temporal KG completion and reasoning.

7. Awesome Multimodal Knowledge Graph

https://github.com/ZihengZZH/awesome-multimodal-knowledge-graph surveys the emerging multimodal KG field, covering concepts (MMKG, IKRL), multimodal entity linking, relation extraction, applications in visual QA and recommendation, multimodal language models, and related tutorials.

The author invites readers to share additional high‑quality KG resources and to follow the "AI meets Knowledge Graph" public account for further updates.