BI-Extended Enterprise Knowledge Graphs

Enterprise Intelligence and Time Molecules link through the Tuple Correlation Web. Introduction The main takeaway of this blog is an explanation of how my two books, Enterprise Intelligence (June 21, 2024) and the more recently published Time Molecules (June 4, 2025), connect. Time Molecules is a follow-up to my first book, Enterprise Intelligence. It connects … Continue reading BI-Extended Enterprise Knowledge Graphs →

Thinking Reliably and Creatively – Prolog in the LLM Era – Summer Vacation Special

Welcome to the Prolog in the LLM Era Summer Vacation Special! Starring … Prolog ... knowledge graphs ... ChatGPT o4-mini ... neuro-symbolic AI ... and our special guest ... Thinking Fast and Slow! In this episode (Part 11 of the series), I wish to address how neuro-symbolic AI relates to this series. After all, the series title, Prolog … Continue reading Thinking Reliably and Creatively – Prolog in the LLM Era – Summer Vacation Special →

Thousands of Senses

In this mind-bogglingly complex world in which we live—countless moving parts fraught with imperfect information of many types—a versatile highly-functioning intelligence requires quick access to a wide variety of information in order to make intelligent decisions. It's not that all decisions should be made from that full breadth of information. It's that all decisions are … Continue reading Thousands of Senses →

From Data through Wisdom: The Case for Process-Aware Intelligence

My new book, Time Molecules, is available!! This is its launch-day blog (June 4, 2025)! Time Molecules is a book about bringing process awareness to business intelligence (BI). Traditional BI flattens reality into snapshots— scalar values, points of information— leaving us staring at dimensionally flattened shadows on a wall. But life unfolds over time, and … Continue reading From Data through Wisdom: The Case for Process-Aware Intelligence →

Trophic Cascades of AI

As I mentioned in a previous post, Sample From My Talk - NFA, I will be delivering two sessions at the Data Modeling Zone 2025 (DMZ) in Phoenix. It will be happening from Tuesday, March 4, 2025 through Thursday, March 6, 2025. That post included a preview one of my two sessions, Beyond Ontologies and Taxonomies—focusing on … Continue reading Trophic Cascades of AI →

Embedding Machine Learning Models into Knowledge Graphs

Think about the usual depiction of a network of brain neurons. It’s almost always shown as a sprawling, kind of amorphous web, with no real structure or organization—just a big ball of connected neurons (like the Griswold Christmas lights). But this image misses so much of what makes the brain remarkable. Neurons aren’t just randomly … Continue reading Embedding Machine Learning Models into Knowledge Graphs →

Prolog Strategy Map – Prolog in the LLM Era – Holiday Season Special

Welcome to the Prolog in the LLM Era Holiday Season Special! Starring … pyswip … SWI-Prolog … the Semantic Web … data mesh … and … special guest star, ChatGPT! Notes: This blog is best consumed by first reading at least Part 1 of the series (preferably the first 3 parts): Prolog in the LLM Era – Part 1. This blog is really Part … Continue reading Prolog Strategy Map – Prolog in the LLM Era – Holiday Season Special →

The BI Counterpart to AI Infinite Context

This post maps the ideas in the recent paper Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention (Munkhdalai, T., Faruqui, M., & Gopal, S. 2024) to concepts from my book, Enterprise Intelligence, including the Tuple Correlation Web (TCW), Insight Space Graph (ISG), and cubespace as a semantic layer. In business intelligence (BI) and … Continue reading The BI Counterpart to AI Infinite Context →

Deductive Time Travel – Prolog in the LLM Era – Thanksgiving Special

The content discusses the significance of historical context in learning and decision-making, emphasizing the value of Prolog as a tool to understand complex logical rules over time. It explores how expert knowledge and decision-making evolve, and how modern technology can facilitate the integration of historical insights into artificial intelligence, enabling enriched decision-making today.

Prolog and ML Models – Prolog’s Role in the LLM Era, Part 4

The blog post discusses the integration of Prolog with large language models (LLMs) and its application in machine learning (ML). It explores the relationship between Prolog and ML models like decision trees, association rules, clustering, linear regression, and logistic regression. It also provides an example of transforming decision tree rules into Prolog using Python.