NLQxform: A Language Model-based Question to SPARQL Transformer
This provides an easy-to-use natural language interface for accessing scholarly knowledge graphs, addressing a domain-specific problem for researchers and users of academic data.
The paper tackles the challenge of retrieving information from complex scholarly knowledge graphs by developing NLQxform, a question-answering system that translates natural language questions into SPARQL queries using a BART transformer model. It achieved an F1 score of 0.85 and ranked first in the Scholarly QALD Challenge at ISWC 2023.
In recent years, scholarly data has grown dramatically in terms of both scale and complexity. It becomes increasingly challenging to retrieve information from scholarly knowledge graphs that include large-scale heterogeneous relationships, such as authorship, affiliation, and citation, between various types of entities, e.g., scholars, papers, and organizations. As part of the Scholarly QALD Challenge, this paper presents a question-answering (QA) system called NLQxform, which provides an easy-to-use natural language interface to facilitate accessing scholarly knowledge graphs. NLQxform allows users to express their complex query intentions in natural language questions. A transformer-based language model, i.e., BART, is employed to translate questions into standard SPARQL queries, which can be evaluated to retrieve the required information. According to the public leaderboard of the Scholarly QALD Challenge at ISWC 2023 (Task 1: DBLP-QUAD - Knowledge Graph Question Answering over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA task, demonstrating the competitiveness of the system.