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- Title
KEI-CQL: A Keyword Extraction and Infilling Framework for Text to Cypher Query Language Translation.
- Authors
Yuan-Lin Liang; Chih-Yung Chang; Shih-Jung Wu
- Abstract
Graph databases are invaluable for structuring and querying interconnected data, but Text-to-Cypher (CQL) remains a challenging frontier. This research paper introduces a pioneering deep learning approach that utilizes pre-trained language models to facilitate the translation of natural language queries into Cypher Query Language. Bridging the gap between these languages is crucial for organizations to manage these specific databases efficiently. This work proposes a novel framework KEI-CQL, that leverages the capabilities of deeplearning models to translate text queries into Neo4j Cypher queries. The framework KEI-CQL can extract the semantic features in the natural language query and fill in the pre-defined Cypher query sketch slots. The results from the experiment show that the proposed framework can convert the SQL query into a Cypher query.
- Subjects
LANGUAGE models; DEEP learning; NATURAL languages; MACHINE translating; TRANSLATING &; interpreting; LANGUAGE &; languages; SQL
- Publication
International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 2024, Vol 13, Issue 1, p21
- ISSN
2071-2987
- Publication type
Article