@inproceedings{mitra-etal-2022-constraint,
title = "Constraint-based Multi-hop Question Answering with Knowledge Graph",
author = "Mitra, Sayantan and
Ramnani, Roshni and
Sengupta, Shubhashis",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.31",
doi = "10.18653/v1/2022.naacl-industry.31",
pages = "280--288",
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.",
}
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%0 Conference Proceedings
%T Constraint-based Multi-hop Question Answering with Knowledge Graph
%A Mitra, Sayantan
%A Ramnani, Roshni
%A Sengupta, Shubhashis
%Y Loukina, Anastassia
%Y Gangadharaiah, Rashmi
%Y Min, Bonan
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F mitra-etal-2022-constraint
%X 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.
%R 10.18653/v1/2022.naacl-industry.31
%U https://aclanthology.org/2022.naacl-industry.31
%U https://doi.org/10.18653/v1/2022.naacl-industry.31
%P 280-288
Markdown (Informal)
[Constraint-based Multi-hop Question Answering with Knowledge Graph](https://aclanthology.org/2022.naacl-industry.31) (Mitra et al., NAACL 2022)
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.