A Tool for Semantic-Aware Spatial Corpus Construction

TL;DR

This paper introduces SSCC, a tool that efficiently constructs high-quality spatial natural language query corpora by combining knowledge base extraction and template-based generation, significantly improving efficiency and effectiveness.

Contribution

The paper presents a novel semantic-aware spatial corpus construction tool that enhances corpus quality and construction efficiency for spatial natural language interfaces.

Findings

53x faster knowledge base construction

2.5x more effective query pair generation

High-quality corpus reduces training costs

Abstract

Spatial natural language interface to database systems provide non-expert users with convenient access to spatial data through natural language queries. However, the scarcity of high-quality spatial natural language query corpora limits the performance of such systems. Existing methods rely on manual knowledge base construction and template-based dynamic generation, which suffer from low construction efficiency and unstable corpus quality. This paper presents semantic-aware spatial corpus construction (SSCC), a tool designed for constructing high-quality spatial natural language query and executable language query pair corpora. SSCC consists of two core modules: (i) a knowledge base construction module based on spatial relations, which extracts and determines spatial relations from datasets, and (ii) a template-augmented query pair corpus generation module, which produces query pairs via template matching and parameter substitution. The tool ensures geometric consistency and adherence to spatial logic in the generated spatial relations. Experimental results demonstrate that SSCC achieves (i) a 53x efficiency improvement for knowledge base construction and (ii) a 2.5x effectiveness improvement for query pair corpus. SSCC provides high-quality corpus support for spatial natural language interface training, substantially reducing both time and labor costs in corpus construction.