Search Results (2)

Despite the growing adoption of Property Graph Databases, like Neo4j, interacting with them remains difficult for non-technical users due to the reliance on formal query languages. Natural Language Interfaces (NLIs) address this by translating natural language (NL) into Cypher. However, existing solutions are typically limited to high-resource languages; are difficult to adapt to evolving domains with limited annotated data; and often depend on Machine Learning (ML) approaches, including Large Language Models (LLMs), that demand substantial computational resources and advanced expertise for training and maintenance. We address these limitations by introducing a novel dependency-based, training-free, schema-agnostic Natural Language Interface (NLI) that converts NL queries into Cypher for querying Property Graphs. Our system employs a modular pipeline-integrating entity and relationship extraction, Named Entity Recognition (NER), semantic mapping, triple creation via syntactic dependencies, and validation against an automatically extracted Schema Graph. The distinctive feature of this approach is the reduction in candidate entity pairs using syntactic analysis and schema validation, eliminating the need for candidate query generation and ranking. The schema-agnostic design enables adaptation across domains and languages. Our system supports single- and multi-hop queries, conjunctions, comparisons, aggregations, and complex questions through an explainable process. Evaluations on real-world queries demonstrate reliable translation results. Full article

Graph databases have become essential for managing and analyzing complex data relationships, with Neo4j emerging as a leading player in this domain. Neo4j, a high-performance NoSQL graph database, excels in efficiently handling connected data, offering powerful querying capabilities through its Cypher query language. However, due to Cypher’s complexities, making it more accessible for nonexpert users requires translating natural language queries into Cypher. Thus, in this paper, we propose a text-to-Cypher model to effectively translate natural language queries into Cypher. In our proposed model, we combine several methods to enable nonexpert users to interact with graph databases using the English language. Our approach includes three modules: key-value extraction, relation–properties prediction, and Cypher query generation. For key-value extraction and relation–properties prediction, we leverage BERT and GraphSAGE to extract features from natural language. Finally, we use a Transformer model to generate the Cypher query from these features. Additionally, due to the lack of text-to-Cypher datasets, we introduced a new dataset that contains English questions querying information within a graph database, paired with corresponding Cypher query ground truths. This dataset aids future model learning, validation, and comparison on text-to-Cypher task. Through experiments and evaluations, we demonstrate that our model achieves high accuracy and efficiency when comparing with some well-known seq2seq model such as T5 and GPT2, with an 87.1% exact match score on the dataset. Full article