Introducing the Awesome Knowledge Graph Reasoning Project
The Awesome Knowledge Graph Reasoning (AKGR) project is an extensive repository dedicated to the world of knowledge graph reasoning, featuring a comprehensive collection of works, including research papers, codebases, and datasets. This project is curated for researchers and developers who want to delve into the intricacies of knowledge graph reasoning, which involves understanding and inferring new information from existing graph-based data structures.
Highlights of the Project
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Collaborative and Open: AKGR not only embraces contributions from the community but also encourages researchers to share and integrate their works. This open platform serves as a resource hub for anyone interested in knowledge graph reasoning.
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Star and Fork Us: The project invites users to support its growth and reach by starring the repository on GitHub. This small gesture greatly supports the project's visibility and future development.
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Contact and Contributions: For queries or contributions, the project provides contact emails for one of the project leaders, promising a collaborative approach to expanding its horizons.
Citing the Survey Paper
The repository references a survey paper titled "A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal," authored by Liang, Ke et al. and published in the prestigious IEEE Transactions on Pattern Analysis and Machine Intelligence. This paper is vital for understanding the theoretical foundations and progress in knowledge graph reasoning and is available for citation to support further academic work.
Repository Structure and Content
Bookmarked Sections:
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Survey Papers: A curated list of survey papers spanning recent advancements and comprehensive overviews in the field of knowledge graph reasoning is available.
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Datasets: Lists of diverse datasets are grouped by graph types such as Static, Temporal, and Multi-Modal Knowledge Graphs, further categorized into Transductive and Inductive datasets for static graphs.
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Models and Algorithms: The project explores various reasoning models, categorized into:
- Static Knowledge Graph Reasoning: Including translational, tensor decomposition, neural network (traditional, convolutional, graph neural networks, transformers), path-based, and rule-based models.
- Temporal Knowledge Graph Reasoning: Primarily categorized by RNN-based and RNN-agnostic models, with subcategories focusing on quadruple, path, and graph-based approaches.
- Multi-Modal Knowledge Graph Reasoning: Detailed exploration of transformer-agnostic and transformer-based models.
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Useful Libraries: The project contains a list of libraries essential for developing and running knowledge graph reasoning algorithms.
Target Audience
AKGR is primarily intended for researchers, data scientists, and AI practitioners interested in developing new models or improving existing methods for knowledge graph reasoning. It serves as both an academic guide and a practical toolkit, offering the resources needed to innovate in this field.
In summary, the AKGR project is a comprehensive collection of resources aiming to aid the research and development of knowledge graph reasoning. With its diverse offerings and collaborative spirit, it stands as a significant contribution to this rapidly growing area of artificial intelligence.