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Towards Data Science
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Text to Knowledge Graph Made Easy with Graph Maker
- This article is a sequel to the article I wrote a few months ago about how to convert any text into a Graph.
- Recently, the aforementioned article was attributed in a paper published by Prof Markus J. Buehler at MIT.
- The article received an overwhelming response with more than 80K readers on the medium and more than 180 Forks and 900 Stars on the GitHub repository shared in the article.
- The article is about making text to knowledge graph very easy with the help of Graph Maker, a new python package.
- The article discusses various challenges encountered, and observations received while extracting graphs with LLMs, which can subsume traditional methods.
- The package enables the creation of large knowledge graphs with as large a corpus of text as desired, using the LLM through prompting and chunking.
- The metadata assists in adding context with every extracted relation from every document, which helps to contextualize relationships across multiple documents.
- An example Python notebook is included in the repository to get started quickly, and the code can be taken for a spin.
- The Graph Maker is primarily useful for RAG applications and can be leveraged by mix Cypher queries and Network algorithms with Semantic Search.
- The article ends by inviting readers to share their use cases and contributions to this open source project, which the author developed for a few of his pet projects.
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