Knowledge graphs are becoming increasingly important in a variety of fields, including artificial intelligence and information science. They are a graphical representation of entities and the relationships between them, allowing for more efficient and effective storage, analysis, and use of information.
In AI, knowledge graphs provide a structured way to represent vast amounts of information and make it easily accessible for use in machine learning tasks such as question answering, recommendation systems, and information retrieval. They also allow for easier integration of data from multiple sources, reducing the time and effort needed to clean and process data.
In the healthcare and life sciences fields, knowledge graphs can play a crucial role in drug discovery and personalized medicine by providing a comprehensive view of biological entities, pathways, and relationships between diseases and treatments.
In general, knowledge graphs offer a unified representation of information, making it easier for people and machines to access and make use of knowledge. As the amount of data continues to grow, the importance of knowledge graphs is only set to increase in the future.
Papers and Materials from All Areas The papers from the database
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective Click
Stephen Bonner,Ian P Barrett,Cheng Ye,Rowan Swiers,Ola Engkvist,Andreas Bender,Charles Tapley Hoyt,William Hamilton
** Building a Knowledge Base from Texts: a Full Practical Example **Click.
- Fabio Chiusano*
Generating Knowledge Graphs with Wikipedia Click
Jye Sawtell-Rickson
Colab
Building a Knowledge Base from Texts: a Full Practical Example Colab, REBEL
This tutorial will help you generate a knowledge graph from some structured datasets such as CSV, JSON and XML. We will use Semantic Web technologies to build our knowledge graph. If you are not familiar with Semantic Web technologies, we provide you with some basic concepts and technologies about Semantic Web based Knowledge Graphs.link
Converting your Knowledge Graph TSV/CSV to a Resource Description Framework (RDF) For Interoperabilitylink
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Towards a knowledge graph for pre-/probiotics and microbiota–gut–brain axis diseaseshttps://www.nature.com/articles/s41598-022-21735-x
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Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layershttps://www.nature.com/articles/s41467-023-39301-y
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Democratizing knowledge representation with BioCypherhttps://www.nature.com/articles/s41587-023-01848-y. The rapid accumulation of biomedical data and the growing popularity of knowledge graphs as a means of representing complex information have led to the development of the BioCypher framework (https://biocypher.org). This tool aims to assist research groups in building their own biomedical knowledge graphs, which can be cost-prohibitive otherwise
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KG-Hub - Building and Exchanging Biological Knowledge Graphshttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad418/7211646?utm_source=authortollfreelink&utm_campaign=bioinformatics&utm_medium=email&guestAccessKey=303add65-c237-4f6e-b590-61aaa1732749&login=false Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking.
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ANet: A Scalable Path-based Reasoning Approach for Knowledge Graphs. https://arxiv.org/pdf/2206.04798.pdf. ANet is a scalable path-based method for knowledge graph reasoning. Inspired by the classical A* algorithm, A*Net learns a neural priority function to select important nodes and edges at each iteration, which significantly reduces time and memory footprint for both training and inference.https://github.com/DeepGraphLearning/AStarNet