Constraint-based Multi-hop Question Answering with Knowledge Graph

Sayantan Mitra, Roshni Ramnani, Shubhashis Sengupta


Abstract
The objective of a Question-Answering system over Knowledge Graph (KGQA) is to respond to natural language queries presented over the KG. A complex question answering system typically addresses one of the two categories of complexity: questions with constraints and questions involving multiple hops of relations. Most of the previous works have addressed these complexities separately. Multi-hop KGQA necessitates reasoning across numerous edges of the KG in order to arrive at the correct answer. Because KGs are frequently sparse, multi-hop KGQA presents extra complications. Recent works have developed KG embedding approaches to reduce KG sparsity by performing missing link prediction. In this paper, we tried to address multi-hop constrained-based queries using KG embeddings to generate more flexible query graphs. Empirical results indicate that the proposed methodology produces state-of-the-art outcomes on three KGQA datasets.
Anthology ID:
2022.naacl-industry.31
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
Month:
July
Year:
2022
Address:
Hybrid: Seattle, Washington + Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
280–288
Language:
URL:
https://aclanthology.org/2022.naacl-industry.31
DOI:
10.18653/v1/2022.naacl-industry.31
Bibkey:
Cite (ACL):
Sayantan Mitra, Roshni Ramnani, and Shubhashis Sengupta. 2022. Constraint-based Multi-hop Question Answering with Knowledge Graph. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 280–288, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
Cite (Informal):
Constraint-based Multi-hop Question Answering with Knowledge Graph (Mitra et al., NAACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.naacl-industry.31.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.naacl-industry.31.mp4
Data
ComplexWebQuestions