@inproceedings{sen-etal-2020-schema,
title = "Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings",
author = "Sen, Jaydeep and
Babtiwale, Tanaya and
Saxena, Kanishk and
Butala, Yash and
Bhatia, Sumit and
Sankaranarayanan, Karthik",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.115",
doi = "10.18653/v1/2020.coling-main.115",
pages = "1334--1345",
abstract = "Natural Language Query interfaces allow the end-users to access the desired information without the need to know any specialized query language, data storage, or schema details. Even with the recent advances in NLP research space, the state-of-the-art QA systems fall short of understanding implicit intents of real-world Business Intelligence (BI) queries in enterprise systems, since Natural Language Understanding still remains an AI-hard problem. We posit that deploying ontology reasoning over domain semantics can help in achieving better natural language understanding for QA systems. In this paper, we specifically focus on building a Schema Aware Semantic Reasoning Framework that translates natural language interpretation as a sequence of solvable tasks by an ontology reasoner. We apply our framework on top of an ontology based, state-of-the-art natural language question-answering system ATHENA, and experiment with 4 benchmarks focused on BI queries. Our experimental numbers empirically show that the Schema Aware Semantic Reasoning indeed helps in achieving significantly better results for handling BI queries with an average accuracy improvement of {\textasciitilde}30{\%}",
}
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%0 Conference Proceedings
%T Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings
%A Sen, Jaydeep
%A Babtiwale, Tanaya
%A Saxena, Kanishk
%A Butala, Yash
%A Bhatia, Sumit
%A Sankaranarayanan, Karthik
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F sen-etal-2020-schema
%X Natural Language Query interfaces allow the end-users to access the desired information without the need to know any specialized query language, data storage, or schema details. Even with the recent advances in NLP research space, the state-of-the-art QA systems fall short of understanding implicit intents of real-world Business Intelligence (BI) queries in enterprise systems, since Natural Language Understanding still remains an AI-hard problem. We posit that deploying ontology reasoning over domain semantics can help in achieving better natural language understanding for QA systems. In this paper, we specifically focus on building a Schema Aware Semantic Reasoning Framework that translates natural language interpretation as a sequence of solvable tasks by an ontology reasoner. We apply our framework on top of an ontology based, state-of-the-art natural language question-answering system ATHENA, and experiment with 4 benchmarks focused on BI queries. Our experimental numbers empirically show that the Schema Aware Semantic Reasoning indeed helps in achieving significantly better results for handling BI queries with an average accuracy improvement of ~30%
%R 10.18653/v1/2020.coling-main.115
%U https://aclanthology.org/2020.coling-main.115
%U https://doi.org/10.18653/v1/2020.coling-main.115
%P 1334-1345
Markdown (Informal)
[Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings](https://aclanthology.org/2020.coling-main.115) (Sen et al., COLING 2020)
ACL