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artificial intelligence

Knowledge Representation: Graphs or Tables?

In this article I dig into the question of whether you should use a traditional RDBMS (Postgres, MySQL etc.) or a graph database (Neo4J, AWS Neptune, OrientDB etc.) as you embark on your next data management and knowledge representation project, especially in this era of Knowledge Graphs and LLMs.

Knowing the strengths and weaknesses of each will help you pick the right tool for the job.

Image from: https://www.biconnector.com/blog/knowledge-graph-vs-relational-database-differences/

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Artificial Intelligence Agents and Enterprise Systems of Record

There’s been a Cambrian explosion in the number of companies working on orchestration of large language model (LLM) tools, agent-based systems, and exposing enterprise data to LLMs, via retrieval augments generation (RAG), or via graph-based approaches (GraphRAG) or Knowledge Graphs.

In this article I explore LLM agent-based systems, particularly as they will be applied to enterprise systems of record.

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Miss Manners with Claude Sonnet

I repeated my experiment with the Miss Manners benchmark using Clause Sonnet (Clause 3.5 Pro). The results were the best I’ve seen so far.

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Knowledge Graphs: RAG is NOT all you need

Over the past few weeks I’ve been researching, and building a framework that combines the power of Large Language Models for text parsing and transformation with the precision of structured data queries over Knowledge Graphs for explainable data retrieval.

In this fourth article of the series (one, two, three) I will show a generic web interface that helps explain how the LLM is using tools and graph queries to answer a wide variety of structured and unstructured questions.

Continue reading “Knowledge Graphs: RAG is NOT all you need” →
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Knowledge Graphs: Question Answering

Over the past few weeks I’ve been researching, and building a framework that combines the power of Large Language Models for text parsing and transformation with the precision of structured data queries over Knowledge Graphs for explainable data retrieval.

In this third article of the series (one, two) I will show you how to combine structured and unstructured semantic queries, and use LLMs to orchestrate question answering over a knowledge graph.

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Miss Manners with Claude Opus

I repeated my experiment with the Miss Manners benchmark using Clause Opus (Clause Pro). The results were better than GPT4, but inferior to Mistral (Large).

Continue reading “Miss Manners with Claude Opus” →
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