Eugene Asahara, Soft Coded Logic
Putting the "I" Back into BI
TL;DR Expert knowledge is more than facts, rules, procedures, or recipes. Its greatest value often appears when the normal procedure runs out: an unfamiliar failure, an ambiguous observation, or a situation for which no one has already written the answer. Experts draw on a mental model of how the world works, form competing explanations, predict … Continue reading Reasoning Under Imperfect Information
This blog is based on a "deleted scene" but is also an update to the technology mentioned in my book, Semantic Webs of Meaning (available on Technics Publications and Amazon)—specifically Chapter 8 on scaling query performance for a knowledge-graph deployment. In that chapter I did not talk about the contribution a performance-oriented semantic layer (ex. … Continue reading Reaching from the Realm of Information into the Realm of Knowledge
TL;DR (Sort of) Since this TL;DR is relatively long for a TL;DR, here is the elevator pitch: Humans and LLMs live largely in rich, subsymbolic internal worlds. Knowledge graphs live in an explicit, serialized world. Intelligence increasingly depends on moving between the two—and continually checking that what was reconstructed still corresponds to what we meant. … Continue reading Symbols Travel, Meaning Is Rebuilt
Applying self-service BI concepts to knowledge graph development. This blog is from the “deleted scenes” of my book, Semantic Webs of Meaning, available on Technics Publications and Amazon (mostly Chapter 10). It is also a continuation of the competency questions discussion begun in Where Should an Enterprise Knowledge Graph Start? Competency Questions: TL;DR Competency questions … Continue reading The Art of Crafting Competency Questions for Knowledge Graphs
I recently became aware of a stance on how to begin a knowledge graph endeavor that is somewhat contradictory to what I prescribe in Semantic Webs of Meaning. This is an important point to clarify. Here is the bullet-point summary: Different Stance: Do not infer what the ontology ought to be merely from the structures … Continue reading Where Should an Enterprise Knowledge Graph Start?
This article is a supplement to my new book, Semantic Webs of Meaning: Building Contextual Knowledge Graphs for Deduction and Integration published by Technics Publications (on Amazon) . This is an extension to the topic, Machine Learning Models as Data Products, on page 173. In that topic, iris.py is an example of a Python program … Continue reading Web of Functions (Transformations)
Inductive Reasoning Meets Deductive Reasoning This article accompanies my new book, Semantic Webs of Meaning: Building Contextual Knowledge Graphs for Deduction and Integration published by Technics Publications. This is an extension to the topic, Machine Learning Models as Data Products, on page 173. In that topic, iris.py is an example of a Python program that … Continue reading Machine Learning Models in Knowledge Graphs
This blog is based on a “deleted scene” of my book, Semantic Webs of Meaning, available on Technics Publications and Amazon. Use the code TP25 for a 25% discount if purchasing on the Technics Publications website. In your journey to building knowledge graphs, this is where knowledge graph people can start to sound like the "Comic book … Continue reading When “Sedans Have Four Doors” Means Five Different Things in RDF
Update August 25, 2026: The supplemental GitHub repo for the Semantic Webs of Meaning is live. The supplemental repo has these main purposes: Post material that is too down into the weeds for the book (ex. install instructions, full appendices, code samples, and tutorials) but is mentioned there. Enable me to expand upon and/or clarify … Continue reading Trailer for My New Book: Semantic Webs of Meaning
Abstract This blog introduces the Explorer Subgraph, a navigational knowledge structure designed to support System ⅈ (the ⅈ as in the imaginary number). I introduced System ⅈ a few weeks ago in my blog, System ⅈ: The Default Mode Network of AGI. The Explorer Subgraph is a background, always-on layer of intelligence that operates below conscious … Continue reading Explorer Subgraph—The Dynamic Cartography of Relation Space
