Graph Queries from Natural Language using Constrained Language Models and Visual Editing
Benedikt Kantz, Kevin Innerebner, Peter Waldert, Stefan Lengauer, Elisabeth Lex, Tobias Schreck

TL;DR
This paper introduces a novel method that allows non-experts to query knowledge graphs using natural language and visual editing, converting natural language into valid SPARQL queries through a constrained language model approach.
Contribution
The paper presents a new constrained language model pipeline that reliably converts natural language into valid SPARQL queries without syntax errors or invalid links.
Findings
Consistently generates valid SPARQL queries from natural language.
Achieves efficient and accurate graph retrieval compared to other methods.
Validated through synthetic query evaluation and a user study.
Abstract
Querying knowledge bases using ontologies is usually performed using dedicated query languages, question-answering systems, or visual query editors for Knowledge Graphs. We propose a novel approach that enables users to query the knowledge graph by specifying prototype graphs in natural language and visually editing them. This approach enables non-experts to formulate queries without prior knowledge of the ontology and specific query languages. Our approach converts natural language to these prototype graphs by utilizing a two-step constrained language model generation based on semantically similar features within an ontology. The resulting prototype graph serves as the building block for further user refinements within a dedicated visual query builder. Our approach consistently generates a valid SPARQL query within the constraints imposed by the ontology, without requiring any…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Graph Neural Networks · Semantic Web and Ontologies
