Enriching Taxonomies Using Large Language Models
arXiv:2602.22213v1 Announce Type: cross Abstract: Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous nodes, reducing their effectiveness in knowledge retrieval. To address this, we present Taxoria, a novel taxonomy enrichment pipeline that leverages Large Language Models (LLMs) to enhance a given taxonomy. Unlike approaches that extract internal LLM taxonomies, Taxoria uses an existing taxonomy as a seed and prompts an LLM to propose candidate nodes for enrichment. These candidates are then validated to mitigate hallucinations and ensure semantic relevance before integration. The final output includes an enriched taxonomy with provenance tracking and visualization of the final merged taxonomy for analysis.
arXiv:2602.22213v1 Announce Type: cross Abstract: Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous nodes, reducing their effectiveness in knowledge retrieval. To address this, we present Taxoria, a novel taxonomy enrichment pipeline that leverages Large Language Models (LLMs) to enhance a given taxonomy. Unlike approaches that extract internal LLM taxonomies, Taxoria uses an existing taxonomy as a seed and prompts an LLM to propose candidate nodes for enrichment. These candidates are then validated to mitigate hallucinations and ensure semantic relevance before integration. The final output includes an enriched taxonomy with provenance tracking and visualization of the final merged taxonomy for analysis.
Executive Summary
The article 'Enriching Taxonomies Using Large Language Models' introduces Taxoria, a novel pipeline designed to enhance existing taxonomies by leveraging Large Language Models (LLMs). Taxoria addresses the limitations of current taxonomies, such as limited coverage and outdated or ambiguous nodes, by using an existing taxonomy as a seed and prompting an LLM to propose candidate nodes for enrichment. These candidates are then validated to ensure semantic relevance and mitigate hallucinations. The final output includes an enriched taxonomy with provenance tracking and visualization capabilities. This approach offers a promising solution for improving knowledge retrieval and information structuring across various domains.
Key Points
- ▸ Taxoria leverages LLMs to enhance existing taxonomies.
- ▸ The pipeline uses an existing taxonomy as a seed for enrichment.
- ▸ Candidate nodes are validated to ensure semantic relevance and mitigate hallucinations.
- ▸ The final output includes provenance tracking and visualization of the enriched taxonomy.
Merits
Innovative Approach
Taxoria introduces a novel method for taxonomy enrichment by leveraging LLMs, which is a significant advancement over traditional methods that rely on manual updates or internal LLM taxonomies.
Validation Mechanism
The validation step ensures that the proposed candidate nodes are semantically relevant and free from hallucinations, which enhances the reliability and accuracy of the enriched taxonomy.
Provenance Tracking
The inclusion of provenance tracking allows users to trace the origins of the enriched nodes, providing transparency and accountability in the enrichment process.
Demerits
Dependence on LLM Quality
The effectiveness of Taxoria is highly dependent on the quality and capabilities of the underlying LLM, which may vary and could introduce biases or inaccuracies.
Validation Complexity
The validation process, while crucial, adds complexity to the pipeline and may require significant computational resources and time, potentially limiting its scalability.
Domain-Specific Limitations
The approach may not be equally effective across all domains, as the performance of LLMs can vary based on the specificity and complexity of the domain in question.
Expert Commentary
The introduction of Taxoria represents a significant step forward in the field of taxonomy enrichment. By leveraging the capabilities of LLMs, the pipeline addresses critical limitations of existing taxonomies, such as limited coverage and outdated nodes. The validation mechanism ensures that the enriched taxonomy remains semantically relevant and free from hallucinations, which is crucial for maintaining the integrity of the information structure. However, the dependence on the quality of the underlying LLM and the complexity of the validation process pose notable challenges. The approach's effectiveness may vary across different domains, necessitating further research and validation. Overall, Taxoria offers a promising solution for improving knowledge retrieval and information structuring, with potential implications for both practical applications and policy development.
Recommendations
- ✓ Further research should focus on evaluating the performance of Taxoria across various domains to assess its generalizability and robustness.
- ✓ Developing more efficient validation mechanisms could enhance the scalability and practical applicability of the Taxoria pipeline.
