Enhancing Text2Cypher with Schema Filtering (2505.05118v1)

Abstract: Knowledge graphs represent complex data using nodes, relationships, and properties. Cypher, a powerful query language for graph databases, enables efficient modeling and querying. Recent advancements in LLMs allow translation of natural language questions into Cypher queries - Text2Cypher. A common approach is incorporating database schema into prompts. However, complex schemas can introduce noise, increase hallucinations, and raise computational costs. Schema filtering addresses these challenges by including only relevant schema elements, improving query generation while reducing token costs. This work explores various schema filtering methods for Text2Cypher task and analyzes their impact on token length, performance, and cost. Results show that schema filtering effectively optimizes Text2Cypher, especially for smaller models. Consistent with prior research, we find that larger models benefit less from schema filtering due to their longer context capabilities. However, schema filtering remains valuable for both larger and smaller models in cost reduction.

Enhancing Text2Cypher with Schema Filtering

The paper "Enhancing Text2Cypher with Schema Filtering" focuses on optimizing the translation of natural language questions into Cypher queries, a process known as Text2Cypher, by employing schema filtering techniques. This research is pivotal in the field of graph databases, where knowledge graphs serve as a crucial tool for representing complex datasets using nodes, relationships, and properties. Cypher, utilized primarily for Neo4j graph databases, is a powerful query language that facilitates efficient modeling and querying of these graph-structured datasets.

Context and Challenges

One significant advancement noted in the paper is the ability of LLMs to translate natural language into database queries, enabling users without expert knowledge to interact with data repositories effectively. In particular, integrating database schemas into LLM prompts for query generation enhances the context comprehension of models. However, as the paper highlights, complex database schemas can introduce unnecessary noise, increase hallucinations during query generation, and elevate computational costs by expanding token length within model prompts.

To address these challenges, the authors propose schema filtering methods that selectively incorporate only relevant schema elements based on the natural language queries being processed. By focusing on the pertinence of schema data related to each query, these techniques aim to improve the efficiency and accuracy of the Text2Cypher application while mitigating token overhead.

Proposed Approaches

The paper introduces five schema filtering methodologies, divided into static and dynamic approaches, to refine schema integration in the Text2Cypher task:

1. Static Methods: These approaches involve extracting the full database schema in predefined formats, known as the Enhanced and Base schemas. These formats provide comprehensive schema views, including nodes, relationships, and properties, thus enabling caching for efficiency purposes.
2. Dynamic Methods: These methods involve pruning the schema dynamically based on the specific query input. The major dynamic approaches include:
   • Pruned by Exact-Match Schema: Retains schema elements that exactly match the words in the input question.
   • NER Masked Pruned by Exact-Match Schema: Utilizes named entity recognition to mask entities prior to applying exact-match pruning.
   • Pruned by Similarity Schema: Employs similarity measures, particularly embedding-based techniques, to filter schema elements closely related to the query.

Experimental Results

Through evaluation, the research finds that schema filtering effectively enhances the token length, performance, and computational cost of Cypher query generation, particularly for smaller LLMs. While larger models generally benefit less from schema filtering due to their extensive context window capabilities, schema filtering remains advantageous in reducing token costs across model sizes.

Experimental results underline the efficacy of schema filtering, with the Pruned by Exact-Match schema exhibiting the most notable improvements in performance metrics. The paper's quantitative analysis illustrates that token reductions facilitate lower operational costs for LLMs, including both vendor payment models and self-hosted configurations.

Implications and Future Work

The findings from this paper present significant implications for the practical use of LLMs in translating natural language into graph database queries, promoting improvements in user accessibility and system cost-efficiency. By refining schema integration strategies, the research opens avenues for more effective use of resources and enhanced query accuracy.

Future work may explore adaptive schema selection techniques tuned to specific model characteristics and investigate how schema filtering impacts the fine-tuning process in NLP models. Additionally, addressing the limitations of heuristic-based filtering approaches can further advance this domain, drawing inspiration from progressive techniques in Text2SQL tasks.

Overall, this work contributes a nuanced perspective to the optimization of Text2Cypher processes within graph database environments, paving the way for continued developments in reducing computational burdens and elevating the precision of automated query generation systems.