Mapping Natural Language into SQL in a NLIDB

Giordani, Alessandra

doi:10.1007/978-3-540-69858-6_46

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…While much research has focused on translating natural languages into query languages (Ngonga Ngomo et al, 2013;Braun et al, 2017;Dubey et al, 2016;Giordani and Moschitti, 2009;Finegan-Dollak et al, 2018;Giordani, 2008;Xu et al, 2017;Zhong et al, 2017), the state-of-the-art systems typically involve a large amount of training data. Therefore, in order to fully utilize these models that translate a natural language (NL) question into query language (QL), one would need to collect large amounts of both NL-QL pairs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deployment of Conversational Natural Language Interfaces over Databases

Colas¹,

Bui²,

Dernoncourt³

et al. 2020

Proceedings of the First Workshop on Natural Language Interfaces

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Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user's natural language questions for questionanswering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural languageto-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.

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Section: Introductionmentioning

confidence: 99%

Efficient Deployment of Conversational Natural Language Interfaces over Databases

Colas¹,

Bui²,

Dernoncourt³

et al. 2020

Proceedings of the First Workshop on Natural Language Interfaces

View full text Add to dashboard Cite

show abstract

“…While much research has focused on translating natural languages into query lan- guages (Ngonga Ngomo et al, 2013;Braun et al, 2017;Dubey et al, 2016;Giordani and Moschitti, 2009;Finegan-Dollak et al, 2018;Giordani, 2008;Xu et al, 2017;Zhong et al, 2017), the state-of-the-art systems typically involve a large amount of training data. Therefore, in order to fully utilize these models that translate a natural language (NL) question into query language (QL), one would need to collect large amounts of both NL-QL pairs.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the First Workshop on Natural Language Interfaces

Awadallah¹,

Su²,

Sun³

et al. 2020

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Knowledge-based question answering (KB-QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB. In this work, we look at answering complex questions which often require combining information from multiple sources. We present a novel KB-QA system, MULTIQUE, which can map a complex question to a complex query pattern using a sequence of simple queries each targeted at a specific KB. It finds simple queries using a neural-network based model capable of collective inference over textual relations in extracted KB and ontological relations in curated KB. Experiments show that our proposed system outperforms previous KB-QA systems on benchmark datasets, ComplexWebQuestions and WebQuestionsSP.

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“…how can we get knowledge from them with natural language like a QA style? A possible solution is the development of natural language interface of Database (NLIDB) [1] .With the help of NLIDB, it is easy for amateur people to operate the databases without professional training. It provides the way of retrieving knowledge based on natural language, and converts user questions to SQL statements of query language, then uses the SQL statements to query the databases for returning information that people need.…”

Section: Introductionmentioning

confidence: 99%

A Chinese Question Answering System Using Web Service on Restricted Domain

Peng

Chen

Huang

2010

2010 International Conference on Artificial Intelligence and Computational Intelligence

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Nowadays, the knowledge base of Question Answering system, usually stores question answering pairs or text content extracted from web pages. But in last decades, in order to improve work efficiency, many enterprises had made large investment on ERP (Enterprise Resource Planning) and MIS (Management Information System). And the systems have accumulated a great deal of useful business knowledge, of which the majority is in relational databases. Therefore, how to retrieval knowledge intelligently from relational databases with nature language makes practical significance. To deal with this situation, with the help of the idea of Box Computing, the paper presents the technology of Question Answering with web service. Considering the features of web service, the paper adopts question patterns matching with web service. And based on similarity calculation of user questions and question patterns, it is able to provide the accurate data service for the purpose of answering. This paper includes two types of web services: web API (Application Programming Interface) and web APP (Application).And it is propitious to reuse web service developed for shortening the time of exploiting.

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Mapping Natural Language into SQL in a NLIDB

Cited by 14 publications

References 6 publications

Efficient Deployment of Conversational Natural Language Interfaces over Databases

Efficient Deployment of Conversational Natural Language Interfaces over Databases

Proceedings of the First Workshop on Natural Language Interfaces

A Chinese Question Answering System Using Web Service on Restricted Domain

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