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Miss Manners with ChatGPT

“Miss Manners” is organizing a dinner party and needs to devise a seating arrangement for her guests. She has a large circular table and will be inviting 16 guests: 8 males and 8 females. Miss Manners is an aging lady of a bygone era and isn’t aware that gender is not binary. She would like to ensure that guests are not seated next to someone of the same gender, and that guests seated next to each other share at least one hobby.

In this article I will examine how ChatGTP fares with this venerable optimisation (or product rules) benchmark and present conclusions.

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Snowy Adventures in Sweden

Just returning from a week of ski touring and winter camping in the beautiful Dalarna region of Sweden. As we did last summer, we entrusted organising gear, guides and logistics to the lovely people of DoTheNorth. 

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Contracts as Promises

Today, I will continue my meander through contract digitisation, taking a deeper look at contractual obligations and rights and the theoretical background of “contracts as promise”, and I begin to outline a data model that captures some of the concepts.

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Taxonomy of Contract Logic

I spend a lot of time talking to colleagues and connections about contracts, and the types of logic we find in contracts and in contract management. In this post I aim to describe my point of view on this topic and to provide some signposts and terminology to help you navigate this complex space.

I will introduce three types of logic we find in contracts:

  1. Contract Definition Logic
  2. Contract Automation Logic
  3. Contract Assembly Logic
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Knowledge Graph in 100 Lines of Code

In this article I am going to show you how easy it is to create your own custom Knowledge Graph using Typescript, Open Source tools and the Neo4J database.

Knowledge graphs are getting lots of attention at the moment, as they are the natural Yin to the Yang of LLMs, providing structured data to chat interfaces, and powering Retrieval Augmented Generation.

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Overview of the MPEG-21 Media Contract Ontology

I was recently passed a pointer to the MPEG-21 Media Contract Ontology (thank you Niall!). This post is a high-level summary of the very informative paper (linked above) by Víctor Rodríguez-Doncela, Jaime Delgadob, Silvia Llorenteb, Eva Rodríguezb and Laurent Boch.

The Media Contract Ontology (MCO) is an OWL ontology formalizing a vocabulary to represent business contracts in the media content industry. MCO contracts are RDF documents using that vocabulary.

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Why Concepts and Clauses?

Variables without concepts are not conceptually grounded.

What does {{amount}} mean in a template? How about {{height}}?

Contrast with a locale-independent concept model / ontology / data model:

concept Loan has-a-property Amount (a Monetary Amount)
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Contract Digitisation Literature Review

Introduction

The conceptual leap from managing contract text and data, to understanding the real-time rights and obligations of the various parties to a contract is a major one! To get even a partial view of the rights and obligations of contractual parties requires creating a computable representation of the logic and workflow inherent/implicit in the contact, as well as tapping into a digital representation of real-world contract events.

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Mastering Contract Efficiency: Reusing Clauses and Templates for Document Generation

The maturity model for contracts and document generation looks like this:

  1. Copy/paste a MS Word document and use find-and-replace to update the contract details (aka deal-points).
  2. Manage templates as separate artefacts, and use a software platform to automatically find-and-replace the template variables with their values, with values supplied by contract managers via web-forms or read via API from external systems
  3. Introduce conditional logic and calculations into the templates: which necessitates a type-system for the variables
  4. To tame the combinatorial explosion in the number of templates, modularize the templates using a set of included micro-templates, often managed as a clause library, dynamically assembling templates based on incoming data
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