# Fcodes update: a kinship encoding framework with F-Tree GUI & LLM inference

## TL;DR

The paper introduces updated Fcodes, a system for encoding family relationships, with a new GUI and AI tools to improve usability and analysis.

## Contribution

The novel contribution is the enhancement of Fcodes with a GUI, CLI improvements, inbreeding estimation, and LLM-based inference from narrative text.

## Key findings

- Fcodes now includes a graphical user interface (F-Tree) and command-line interface improvements for easier kinship encoding.
- A method for estimating inbreeding coefficients using Fcodes was introduced.
- Large language models can automatically infer family relationships from narrative text using Fcodes.

## Abstract

Family structures play a crucial role in personal development, social dynamics, and mental health. Traditional systems for encoding genealogical data, such as Ahnentafel and the Register System, offer methods to document lineage but face limitations, particularly in accommodating horizontal relationships or handling changes in family datasets. Modern computational systems like LINKAGE and PED, while powerful for genetic analysis, lack human readability and are challenging to apply in fields where unstructured, narrative data is common, such as sociology or psychiatry. This paper aims to bridge this gap by enhancing Fcodes, a flexible and intuitive algorithm for encoding kinship relationships that is suited for both manual and computational use. Building on our previous work, we present improvements to the Fcodes core algorithm and command-line interface (CLI), as well as the development of F-Tree, a new graphical user interface (GUI) to streamline the encoding process. Additionally, we introduce a method for estimating the coefficient of inbreeding using Fcodes and explore the application of artificial intelligence, namely large language models (LLMs), to automatically infer family relationships from narrative text. These advancements highlight the potential of Fcodes in a wide range of research contexts, from social studies to genetics and mental health research.