Augmenting Knowledge Graph Hierarchies Using Large Language Models

Core Concepts

This work leverages large language models to generate and augment hierarchies in an existing knowledge graph, leading to significant increases in hierarchy coverage for intent and color nodes.

Abstract

The authors present a novel approach to automatically generate intricate graph hierarchies in knowledge graphs (KGs) by leveraging neural transformers. They enhance the structure of their KG by generating hierarchies for both intent and color node types, resulting in a 98% increase in hierarchy coverage for intents and 99% for colors.
The key steps are:

Create top-level (L1) categories for the node types to be hierarchized, either manually or using a language model.
Use a classifier module (leveraging few-shot prompting) to assign all KG nodes to one or more L1 categories.
Use a generator module to enhance the existing hierarchy (if any) with the newly added nodes. Two approaches are explored:

Cyclical Generation: Generate each level of the hierarchy in a loop, needed for large taxonomies.
One-Shot Generation: Add all candidate nodes to the hierarchy in a single pass, better for smaller (<100,000 node) taxonomies.

The generated hierarchies are evaluated through a human-in-the-loop approach, with domain experts reviewing the relevance and accuracy. The hierarchical KG is then leveraged to enhance search and recommendation features in Adobe Express.

Stats

The knowledge graph had over 12,000 intent nodes and over 100,000 nodes in total before the hierarchy generation.
After the hierarchy generation, the coverage increased by 98% for intents and 99% for colors.

Quotes

"Hierarchies have key benefits to our users. Organizational Structure: Hierarchical relationships makes it easier to navigate and comprehend the KG. Hierarchies help maintain order and provide a clear understanding of how different concepts are related to each other. Semantic Relationships: Rich intent hierarchies allow us to capture the semantic relationships between concepts. They also help us unlock key features, such as powering browse and SEO relationships. Scalability and Flexibility: Top level categories allow for easier addition of new intents without disrupting the overall structure as the KG grows."

Key Insights Distilled From

Augmenting Knowledge Graph Hierarchies Using Neural Transformers

by Sanat Sharma... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08020.pdf

Deeper Inquiries

How can the one-shot generation approach be further improved to handle larger knowledge graphs without sacrificing accuracy?

In order to enhance the one-shot generation approach for larger knowledge graphs while maintaining accuracy, several strategies can be implemented. Firstly, implementing a more sophisticated filtering mechanism to prioritize nodes based on relevance and importance can help reduce the number of nodes processed in a single pass, thereby improving efficiency. Additionally, incorporating a hierarchical batching technique where nodes are grouped based on their relationships can optimize the generation process for larger graphs. Moreover, introducing a feedback loop mechanism that allows for iterative refinement of the generated hierarchies can help address any inaccuracies or inconsistencies that may arise during the initial generation. By iteratively refining the hierarchies based on feedback from domain experts or automated evaluation metrics, the one-shot generation approach can be fine-tuned to handle larger knowledge graphs effectively.

What are the potential drawbacks or limitations of using large language models for knowledge graph hierarchy generation, and how can they be addressed?

While large language models (LLMs) offer significant advantages in knowledge graph hierarchy generation, they also come with certain drawbacks and limitations. One key limitation is the potential for error propagation, where inaccuracies in the generated hierarchies can be amplified through the recursive nature of the generation process. To address this, implementing additional validation steps or incorporating human oversight to identify and correct errors can help mitigate the impact of error propagation. Another limitation is the challenge of categorizing nodes into the "Other" category, as LLMs tend to struggle with this classification. Developing specialized techniques or prompts to improve the handling of nodes that do not fit into existing categories can enhance the accuracy of the hierarchy generation process. Furthermore, addressing the issue of order importance in node categorization by introducing randomized ordering or prioritization strategies can help reduce biases and improve the overall quality of the generated hierarchies.

How can the generated hierarchies be leveraged to enhance other downstream applications beyond search and recommendation, such as question answering or knowledge-powered decision making?

The generated hierarchies in knowledge graphs can be leveraged to enhance various downstream applications beyond search and recommendation, such as question answering and knowledge-powered decision making. In question answering systems, the hierarchies can be utilized to provide contextually relevant answers by mapping user queries to specific nodes in the hierarchy and retrieving information based on the semantic relationships between nodes. Additionally, in knowledge-powered decision making, the hierarchies can serve as a structured framework for organizing and analyzing data, enabling more informed and efficient decision-making processes. By incorporating the hierarchies into natural language processing models and decision support systems, organizations can benefit from improved data organization, semantic understanding, and contextual relevance, leading to enhanced decision-making capabilities across various domains.