Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Creating a Fine Grained Entity Type Taxonomy Using LLMs

Michael Gunn, Dohyun Park, Nidhish Kamath

TL;DR

The paper examines whether GPT-4 can autonomously construct a large, fine-grained entity type taxonomy by starting from broad categories and refining through iterative prompting. It documents an initial attempt using UltraFine-type inputs, then pivots to an unrestricted, iterative approach that leverages file-backed context and subtree-focused expansion, achieving 5000+ types with depth up to 10. The resulting taxonomy demonstrates high subjective quality and has practical implications for information extraction tasks such as relation extraction and event argument extraction, while enabling pattern-based branch combinations to improve coverage. The work showcases GPT-4's potential to automate structured knowledge representations and to support domain-specific ontologies, presenting a scalable methodology with broad applicability in computational linguistics and AI research.

Abstract

In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. Our objective is to construct a comprehensive taxonomy, starting from a broad classification of entity types - including objects, time, locations, organizations, events, actions, and subjects - similar to existing manually curated taxonomies. This classification is then progressively refined through iterative prompting techniques, leveraging GPT-4's internal knowledge base. The result is an extensive taxonomy comprising over 5000 nuanced entity types, which demonstrates remarkable quality upon subjective evaluation. We employed a straightforward yet effective prompting strategy, enabling the taxonomy to be dynamically expanded. The practical applications of this detailed taxonomy are diverse and significant. It facilitates the creation of new, more intricate branches through pattern-based combinations and notably enhances information extraction tasks, such as relation extraction and event argument extraction. Our methodology not only introduces an innovative approach to taxonomy creation but also opens new avenues for applying such taxonomies in various computational linguistics and AI-related fields.

Creating a Fine Grained Entity Type Taxonomy Using LLMs

TL;DR

The paper examines whether GPT-4 can autonomously construct a large, fine-grained entity type taxonomy by starting from broad categories and refining through iterative prompting. It documents an initial attempt using UltraFine-type inputs, then pivots to an unrestricted, iterative approach that leverages file-backed context and subtree-focused expansion, achieving 5000+ types with depth up to 10. The resulting taxonomy demonstrates high subjective quality and has practical implications for information extraction tasks such as relation extraction and event argument extraction, while enabling pattern-based branch combinations to improve coverage. The work showcases GPT-4's potential to automate structured knowledge representations and to support domain-specific ontologies, presenting a scalable methodology with broad applicability in computational linguistics and AI research.

Abstract

In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. Our objective is to construct a comprehensive taxonomy, starting from a broad classification of entity types - including objects, time, locations, organizations, events, actions, and subjects - similar to existing manually curated taxonomies. This classification is then progressively refined through iterative prompting techniques, leveraging GPT-4's internal knowledge base. The result is an extensive taxonomy comprising over 5000 nuanced entity types, which demonstrates remarkable quality upon subjective evaluation. We employed a straightforward yet effective prompting strategy, enabling the taxonomy to be dynamically expanded. The practical applications of this detailed taxonomy are diverse and significant. It facilitates the creation of new, more intricate branches through pattern-based combinations and notably enhances information extraction tasks, such as relation extraction and event argument extraction. Our methodology not only introduces an innovative approach to taxonomy creation but also opens new avenues for applying such taxonomies in various computational linguistics and AI-related fields.
Paper Structure (18 sections, 7 figures)

This paper contains 18 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Background
  3. UltraFine Entity Typing Task
  4. Methodology
  5. Initial Approach
  6. Limitations of Initial Approach
  7. Initial Solutions
  8. Final Approach
  9. Iterative Process
  10. Extending to Domain Specific Taxonomies
  11. Evaluation
  12. Limitations
  13. Ontology Combinations
  14. Practical Applications
  15. Future Work
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Initial Approach Prompt
  • Figure 2: GPT-4 matching phrases on String Similarity
  • Figure 3: Revised prompt with seed ontology and other explicit instructions
  • Figure 4: Prompt expanding specific subtree
  • Figure 5: GPT-4 Generated Ontology subtrees
  • ...and 2 more figures