Year: 2025
Type: conference-paper
Abstract: Knowledge Graphs (KGs) have gained popularity as a means of storing structured data, with property graphs, in particular, gaining traction in recent years.Consequently, the task of semantic parsing remains crucial in enabling access to the information in these graphs via natural language queries.However, annotated data is scarce, requires significant effort to create, and is not easily transferable between different graphs.To address these challenges we introduce SPOT, a method to generate training data for semantic parsing over Property Graphs without human annotations.We generate tree patterns, match them to the KG to obtain a query program, and use a finite-state transducer to produce a proto-natural language realization of the query.Finally, we paraphrase the proto-NL with an LLM to generate samples for training a semantic parser.We demonstrate the effectiveness of SPOT on two property graph benchmarks utilizing the Cypher query language.In addition, we show that our approach can also be applied effectively to RDF graphs.
Authors: Francesco Cazzaro, Justin Kleindienst, Sofia Márquez Gomez, Ariadna Quattoni
Institution: Universitat Politècnica de Catalunya
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G434337789
OpenAlex ID: W4412888487