@inproceedings{ikbal-etal-2023-self,
title = "Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge",
author = "Ikbal, Shajith and
Sharma, Udit and
Karanam, Hima and
Neelam, Sumit and
Luss, Ronny and
Sreedhar, Dheeraj and
Kapanipathi, Pavan and
Khan, Naweed and
Erwin, Kyle and
Makondo, Ndivhuwo and
Abdelaziz, Ibrahim and
Fokoue, Achille and
Gray, Alexander and
Crouse, Maxwell and
Chaudhury, Subhajit and
Subramanian, Chitra",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.117",
doi = "10.18653/v1/2023.findings-emnlp.117",
pages = "1707--1718",
abstract = "We present a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. These rules define natural language text predicates as a weighted mixture of knowledge base paths. The weights learned during training effectively serve the mapping needed to perform relation linking. We use popular masked training strategy to self-learn the rules. A key distinguishing aspect of our work is that the masked training operate over logical forms of the sentence instead of their natural language text form. This offers opportunity to extract extended context information from the structured knowledge source and use that to build robust and human readable rules. We evaluate accuracy and usefulness of such learned rules by utilizing them for prediction of missing kinship relation in CLUTRR dataset and relation linking in a KBQA system using SWQ-WD dataset. Results demonstrate the effectiveness of our approach - its generalizability, interpretability and ability to achieve an average performance gain of 17{\%} on CLUTRR dataset.",
}
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<abstract>We present a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. These rules define natural language text predicates as a weighted mixture of knowledge base paths. The weights learned during training effectively serve the mapping needed to perform relation linking. We use popular masked training strategy to self-learn the rules. A key distinguishing aspect of our work is that the masked training operate over logical forms of the sentence instead of their natural language text form. This offers opportunity to extract extended context information from the structured knowledge source and use that to build robust and human readable rules. We evaluate accuracy and usefulness of such learned rules by utilizing them for prediction of missing kinship relation in CLUTRR dataset and relation linking in a KBQA system using SWQ-WD dataset. Results demonstrate the effectiveness of our approach - its generalizability, interpretability and ability to achieve an average performance gain of 17% on CLUTRR dataset.</abstract>
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%0 Conference Proceedings
%T Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge
%A Ikbal, Shajith
%A Sharma, Udit
%A Karanam, Hima
%A Neelam, Sumit
%A Luss, Ronny
%A Sreedhar, Dheeraj
%A Kapanipathi, Pavan
%A Khan, Naweed
%A Erwin, Kyle
%A Makondo, Ndivhuwo
%A Abdelaziz, Ibrahim
%A Fokoue, Achille
%A Gray, Alexander
%A Crouse, Maxwell
%A Chaudhury, Subhajit
%A Subramanian, Chitra
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ikbal-etal-2023-self
%X We present a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. These rules define natural language text predicates as a weighted mixture of knowledge base paths. The weights learned during training effectively serve the mapping needed to perform relation linking. We use popular masked training strategy to self-learn the rules. A key distinguishing aspect of our work is that the masked training operate over logical forms of the sentence instead of their natural language text form. This offers opportunity to extract extended context information from the structured knowledge source and use that to build robust and human readable rules. We evaluate accuracy and usefulness of such learned rules by utilizing them for prediction of missing kinship relation in CLUTRR dataset and relation linking in a KBQA system using SWQ-WD dataset. Results demonstrate the effectiveness of our approach - its generalizability, interpretability and ability to achieve an average performance gain of 17% on CLUTRR dataset.
%R 10.18653/v1/2023.findings-emnlp.117
%U https://aclanthology.org/2023.findings-emnlp.117
%U https://doi.org/10.18653/v1/2023.findings-emnlp.117
%P 1707-1718
Markdown (Informal)
[Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge](https://aclanthology.org/2023.findings-emnlp.117) (Ikbal et al., Findings 2023)
ACL
- Shajith Ikbal, Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar, Pavan Kapanipathi, Naweed Khan, Kyle Erwin, Ndivhuwo Makondo, Ibrahim Abdelaziz, Achille Fokoue, Alexander Gray, Maxwell Crouse, Subhajit Chaudhury, and Chitra Subramanian. 2023. Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1707–1718, Singapore. Association for Computational Linguistics.