Knowledge graphs
Building Knowledge Graphs¶
Knowledge graphs can be created with the help of Generative AI. Understanding relationships between pieces of information allows the technology to create visual representations of connections, improving information processing.
General approaches¶
Natural Language is All a Graph Needs is a very powerful manner of fusing LLMs with KGs using natural language
- Node classification and self-supervised link predictions.
- Scaleable natural-English graph prompts for instruction tuning
- Identifying a central node and doing neighbor sampling and explorations using LLMs.
- Avoids complex attention mechanisms and tokenizers.
Title: GPT Graph: A Simple Tool for Knowledge Graph Exploration
A knowledge graph is a type of database that is used to store and represent knowledge in a machine-readable format. It uses a graph-based model, consisting of nodes (entities) and edges (relationships), to represent information and the connections between them. Knowledge graphs are often used to represent complex information in a structured and intuitive way, making it easier for machines to understand and analyze. They can be used in various domains, such as natural language processing, search engines, recommendation systems, and data analytics.
It’s a unique way to explore information in an organized and intuitive manner. With GPT Graph, you can easily navigate through different topics, discover new relationships between them, and generate creative ideas.
It leverages the power of GPT-3 to generate relevant and high-quality content. Unlike traditional keyword-based searches, GPT Graph takes a more semantic approach to explore the topics and generate the graph. It helps to uncover hidden relationships between different topics and provides a comprehensive view of the entire knowledge domain.
Moreover, GPT Graph provides a user-friendly interface that allows users to interact with the graph easily. Users can ask questions, generate prompts, and add their own ideas to the graph. It’s a powerful tool that enables users to collaborate, brainstorm, and generate new insights in a very efficient way. .
Description of Graphs for LLMs¶
[GPT4Graph: Can Large Language Models Understand Graph sTructure Data? An Empirical Evaluation and Benchmarking"]
Other examples¶
Enhancing LLMs with Semantic-layers
Blog Enhancing Interaction between Language Models and Graph Databases via a Semantic Layer
"Knowledge graphs provide a great representation of data with flexible data schema that can store structured and unstructured information. You can use Cypher statements to retrieve information from a graph database like Neo4j. One option is to use LLMs to generate Cypher statements. While that option provides excellent flexibility, the truth is that base LLMs are still brittle at consistently generating precise Cypher statements. Therefore, we need to look for an alternative to guarantee consistency and robustness. What if, instead of developing Cypher statements, the LLM extracts parameters from user input and uses predefined functions or Cypher templates based on the user intent? In short, you could provide the LLM with a set of predefined tools and instructions on when and how to use them based on the user input, which is also known as the semantic layer."
OntoGPT uses two different methods to query knowledge graphs using LLMS
Uses SPIRES: Structured Prompt Interrogation and Recursive Extraction of Semantics A Zero-shot learning (ZSL) approach to extracting nested semantic structures from text This approach takes two inputs - 1) LinkML schema 2) free text, and outputs knowledge in a structure conformant with the supplied schema in JSON, YAML, RDF or OWL formats Uses GPT-3.5-turbo, GPT-4, or one of a variety of open LLMs on your local machine SPINDOCTOR: Structured Prompt Interpolation of Narrative Descriptions Or Controlled Terms for Ontological Reporting
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals proposes a set of preprocessing operators that can transform KGs to be embedded within any method.
Other Papers and utilities¶
Multimodal learning with graphs
Preprint Nature While not strictly GenAI focused, this introduces a comprehensive manner of combining cross-modal dependencies using geometric relationships.