A Comparative Analysis of Natural Language Interfaces to Databases: Traditional and Neural Network-Based Approaches
Natural language interfaces for databases (NLIDBs) have gained significant attention in recent years due to their potential to make databases more accessible to non-technical users. By allowing users to interact with databases using natural language queries, NLIDBs eliminate the need for users to learn complex query languages like SQL. This paper aims to provide a comparative analysis of traditional NLIDBs and the more recent neural natural language interfaces for databases (NNLIDBs), highlighting their key characteristics, advantages, limitations, and potential applications.
Traditional NLIDBs are systems that interpret natural language queries and translate them into structured database queries using rule-based or logic-based approaches. These systems typically rely on a set of predefined rules and patterns to understand the syntax and semantics of natural language queries. Some well-known examples of traditional NLIDBs include LUNAR (Woods, 1973) and LADDER (Hendrix et al., 1978). While these systems have been successful in specific domains, they often struggle with the ambiguity and complexity of natural language, requiring users to adhere to specific syntax or vocabulary to ensure accurate query interpretation.
NNLIDBs, on the other hand, leverage neural network-based techniques and machine learning to interpret natural language queries. These systems are trained on large datasets of natural language queries and their corresponding database queries, allowing them to learn the mapping between the two. By utilizing deep learning architectures such as sequence-to-sequence models (Sutskever et al., 2014) and attention mechanisms (Bahdanau et al., 2015), NNLIDBs can handle a wider range of natural language variations and are more robust to ambiguity. Examples of NNLIDB systems include Seq2SQL (Zhong et al., 2017) and SQLNet (Xu et al., 2017), which have shown promising results in translating natural language queries to SQL.
Flexibility and robustness are two key areas where NNLIDBs excel compared to traditional NLIDBs. NNLIDBs can handle a wider range of natural language inputs and are less dependent on strict syntax or vocabulary. This makes them more user-friendly and accessible to non-technical users who may not be familiar with the specific query language or database schema. In terms of performance and accuracy, NNLIDBs have shown competitive results, often outperforming traditional NLIDBs in benchmark datasets (Affolter et al., 2019). However, the interpretability of NNLIDBs can be a challenge, as the learned mappings between natural language and database queries are often opaque and difficult to explain.
NNLIDBs have the potential to benefit various domains where users need to access and analyze data stored in databases. In the business intelligence domain, NNLIDBs can enable non-technical users to ask questions and obtain insights from company databases without relying on IT professionals. In the healthcare domain, NNLIDBs can facilitate medical professionals’ access to patient records and clinical data, enabling them to make informed decisions. Additionally, NNLIDBs can be valuable in educational settings, allowing students to explore and learn from databases using natural language queries.
Despite the advancements in NNLIDB technology, several challenges remain to be addressed. Improving the interpretability of NNLIDBs is crucial for building trust and ensuring the reliability of the generated queries. Techniques such as attention visualization (Xu et al., 2015) and rule extraction (Ribeiro et al., 2016) can help in this regard. Handling complex queries that involve multiple tables, nested subqueries, and aggregations is another challenge that requires further research. Integrating NNLIDBs with other AI technologies, such as knowledge graphs and natural language generation, can enhance their capabilities and enable more sophisticated question answering. Finally, addressing data privacy and security concerns is essential when deploying NNLIDBs in real-world applications.
This paper has provided a comparative analysis of traditional NLIDBs and NNLIDBs, highlighting the advantages of NNLIDBs in terms of flexibility, robustness, and user-friendliness. NNLIDBs have the potential to democratize access to databases and enable non-technical users to obtain insights from data. However, challenges such as interpretability, handling complex queries, and data privacy need to be addressed to fully realize the potential of NNLIDBs. Continued research and development in this field are crucial to improve the performance and usability of NNLIDBs and to make them a viable solution for natural language database access in various domains.
Resources:
- nldb (applications of natural language to data bases) .. dblp conferences and workshops
- nldb.org .. international conference on natural language and information systems
Wikipedia:
References:
- BioGraphBot: A Conversational Assistant for Bioinformatics Graph Databases (2017)
- Generating chat bots from web API specifications (2017)
- Semantic Structures: Advances in Natural Language Processing (2015)
See also:
Natural Language & SQL | Natural Language Interface
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Natural language interface for querying hardware and software configuration of a local area network
P Naik, SG Patil, G Naik – Int. J. Comput. Sci. Eng., 2019 – researchgate.net
… split(“ “); query = ”[“; for (i=0;i<tokens.length;i++) { query=query + tokens[i]; } q1 = new Query(“consult(‘hqlgrammar.pl’)”); q2 = new Query(query); if (q2.query()== 1) return 1; else return 0; } /* A function for evaluating the query using Natural Language Interface to Database …
Char-RNN and Active Learning for Hashtag Segmentation
T Glushkova, E Artemova – arXiv preprint arXiv:1911.03270, 2019 – arxiv.org
… Utama, Prasetya and Weir, Nathaniel and Basik, Fuat and Binnig, Carsten and Cetintemel, Ugur and Hättasch, Benjamin and Ilkhechi, Amir and Ramaswamy, Shekar and Usta, Arif: An End-to-end Neural Natural Language Interface for Databases (2018) 18 …
Syntax and Table Aware Parsing Based Naturalized Structured Query Language
PM Veerappa, AA Chikkamannur – inass.org
Page 1. Received: January 27, 2019 138 International Journal of Intelligent Engineering and Systems, Vol.12, No.3, 2019 DOI: 10.22266/ijies2019.0630.15 Syntax and Table Aware Parsing Based Naturalized Structured Query Language …
A comparative survey of recent natural language interfaces for databases
K Affolter, K Stockinger, A Bernstein – The VLDB Journal, 2019 – Springer
Over the last few years, natural language interfaces (NLI) for databases have gained significant traction both in academia and industry. These systems use very different approaches as described in…
New Intelligent Tools to Adapt NL Interface to Corporate Environments
S Chuprina, I Postanogov – International Conference on Computational …, 2019 – Springer
… Semant. Web 6(2), 167–195 (2015). https://doi.org/10.3233/SW-140134CrossRefGoogle Scholar. 10. Nihalani, N., Silakari, S., Motwani, M.: Natural language interface for database: a brief review. Int. J. Comput. Sci. Issues (IJCSI) 8(2), 600–608 (2011)Google Scholar. 11 …
Automatic Comprehension of Customer Queries for Feedback Generation
NE Okwunma – 2019 – academia.edu
… It provides user with the relevant answers to the user questions using Natural Language pro- cessing (NLP) and Natural language interface for database (NLIDB). NLIDB are the systems that translate a natural language sentence into a database query. It …
Transferable natural language interface to structured queries aided by adversarial generation
H Xiong, R Sun – 2019 IEEE 13th International Conference on …, 2019 – ieeexplore.ieee.org
… bases,” arXiv preprint arXiv:1702.01569, 2017. [26] W. Wang, Y. Tian, H. Xiong, H. Wang, and W.-S. Ku, “A transfer-learnable natural language interface for databases,” arXiv preprint arXiv:1809.02649, 2018. [27] I. Gur, S. Yavuz, Y …
GUP: A cognitive initiative to identify relationships between academia and industry
D Jaramillo, B Lakhiyani, VVR Chinta – 2019 SoutheastCon, 2019 – ieeexplore.ieee.org
… arXiv:0912.2282 (2009). [8] Verma, Preeti, Suket Arora, and Kamaljit Batra. “Punjabi Language Interface to Database: a brief review.” arXiv preprint arXiv:1306.4139 (2013). [9] IBM Bluemix. https://www.ibm.com/cloud-computing/bluemix/ [10] IBM Watson Cognitive …
Nl2PSQL: Generating pseudo-SQL queries from under-specified natural language questions
F Chen, S Hwang, J Choo, JW Ha, S Kim – Proceedings of the 2019 …, 2019 – aclweb.org
Page 1. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 2603–2613, Hong Kong, China, November 3–7, 2019 …
Data-Anonymous encoding for Text-to-SQL generation
Z Dong, S Sun, H Liu, JG Lou, D Zhang – Proceedings of the 2019 …, 2019 – aclweb.org
Page 1. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5405–5414, Hong Kong, China, November 3–7, 2019 …
Extracting declarative process models from natural language
H van der Aa, C Di Ciccio, H Leopold… – … Conference on Advanced …, 2019 – Springer
Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and…
SchenQL–A Domain-Specific Query Language on Bibliographic Metadata
CK Kreutz, M Wolz, R Schenkel – arXiv preprint arXiv:1906.06132, 2019 – arxiv.org
Page 1. SchenQL – A Domain-Specific Query Language on Bibliographic Metadata Extended Version Christin Katharina Kreutz[0000?0002?5075?7699], Michael Wolz[0000?0002?9313?7131], and Ralf Schenkel[0000?0001?5379?5191] …
How computational tools can help rhetoric and informal logic with argument invention
D Walton, TF Gordon – Argumentation, 2019 – Springer
… Argument is a relation between claim and topic. 4. Invents arguments using a knowledge base of schemes and assumptions, represented in a formal knowledge-representation language. Searches a natural language database. 5 …
Aspect and tense attrition in Russian-German bilingual speakers
O Dragoy, E Virfel, A Yurchenko… – International Journal …, 2019 – journals.sagepub.com
Purpose:The impact of second language (L2) on first language (L1), known as L2 transfer, has been suggested as a fundamental driving force of L1 attrition. The goal of this study was to test the di…
Verifying text summaries of relational data sets
S Jo, I Trummer, W Yu, X Wang, C Yu, D Liu… – Proceedings of the 2019 …, 2019 – dl.acm.org
Page 1. Verifying Text Summaries of Relational Data Sets Saehan Jo Cornell University sj683@cornell.edu Immanuel Trummer Cornell University itrummer@cornell.edu Weicheng Yu Cornell University wy248@cornell.edu Xuezhi Wang Google Research xuezhiw@google.com …
Formation of SQL from natural language query using NLP
M Uma, V Sneha, G Sneha, J Bhuvana… – … Intelligence in Data …, 2019 – ieeexplore.ieee.org
… [5] Huang, Bei-Bei, Zhang, Guigang et al., (2008) “A natural language database interface based … [9] El-Mouadib, Faraj A, Zubi, Zakaria S, Almagrous, Ahmed A, El-Feghi and Irdess S, (2009) “Generic interactive natural language interface to databases (GINLIDB),” International …
Hybrid Deep Question Answering
A Aghaebrahimian – 2019 – dspace.cuni.cz
Page 1. DOCTORAL THESIS Ahmad Aghaebrahimian Hybrid Deep Question Answering Institute of Formal and Applied Linguistics Supervisor of the doctoral thesis: RNDr. Martin Holub, Ph.D. Study programme: Computer Science Study branch: Mathematical Linguistics (4I3) …
Learning Natural Language Interfaces using Deep Neural Networks
I GUR – 2019 – escholarship.org
Page 1. UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations Title Learning Natural Language Interfaces using Deep Neural Networks Permalink https://escholarship.org/ uc/item/70r066q9 Author GUR, IZZEDDIN Publication Date 2019 …
Strategic WIS Modelling
KD Schewe, B Thalheim – Design and Development of Web Information …, 2019 – Springer
Page 1. 2 Strategic WIS Modelling The goal of this chapter is to discuss modelling on the strategic layer of a WIS, an issue that is often enough neglected. Strategic modelling comprises two parts. The first one addresses a very …
Fast converging cuckoo search algorithm to design symmetric FIR filters
P Das, SK Naskar, S Narayan Patra – International Journal of …, 2019 – Taylor & Francis
Elimination of noise from transmitted signals inevitably incorporated during transmission persisted important task for the researchers from the preliminary days of Signal Processing. Among differen…
Developing Conversational Natural Language Interface to a Database
B Galitsky – Developing Enterprise Chatbots, 2019 – Springer
In this Chapter we focus on a problem of a natural language access to a database, well-known and highly desired to be solved. We start with the modern approaches based on deep learning and analyze…
SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data
J Tekli, R Chbeir, AJM Traina, C Traina Jr – Knowledge-Based Systems, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
Design and development of Web information systems
KD Schewe, B Thalheim – 2019 – Springer
Page 1. Klaus-Dieter Schewe Bernhard Thalheim Design and Development of Web Information Systems Page 2. Design and Development of Web Information Systems Page 3. Klaus-Dieter Schewe • Bernhard Thalheim Design and Web Information Development of Systems …
Encounters with HCI Pioneers: A Personal History and Photo Journal
B Shneiderman – Synthesis Lectures on Human-Centered …, 2019 – morganclaypool.com
Page 1. SH NE IDE RMAN E NC O UN T E R S WIT H HCI P IONE E R S M O R GAN & CL A YPOO L Page 2. Page 3. Encounters with HCI Pioneers A Personal History and Photo Journal Page 4. Page 5. Synthesis Lectures on Human-Centered Informatics …
Data-driven assistance for user decision making on mobile devices
X Liu – 2019 – ideals.illinois.edu
Page 1. © 2019 Susan Xueqing Liu Page 2. DATA-DRIVEN ASSISTANCE FOR USER DECISION MAKING ON MOBILE DEVICES BY SUSAN XUEQING LIU DISSERTATION Submitted in partial fulfillment of the requirements …