Natural Language Querying System Through Entity Enrichment

• URL: http://arxiv.org/abs/2410.15753v1
• Date: Mon, 21 Oct 2024 08:11:47 GMT
• Title: Natural Language Querying System Through Entity Enrichment
• Authors: Joshua Amavi, Mirian Halfeld Ferrari, Nicolas Hiot,
• Abstract summary: This paper focuses on a domain expert querying system over databases. It presents a solution designed for a French enterprise interested in offering a natural language interface for its clients.
• Score: 0.0
• License:
• Abstract: This paper focuses on a domain expert querying system over databases. It presents a solution designed for a French enterprise interested in offering a natural language interface for its clients. The approach, based on entity enrichment, aims at translating natural language queries into database queries. In this paper, the database is treated through a logical paradigm, suggesting the adaptability of our approach to different database models. The good precision of our method is shown through some preliminary experiments.

Related papers

• Natural Language Query Engine for Relational Databases using Generative AI [0.0]
  This article introduces an innovative solution that leverages Generative AI to bridge the gap, enabling users to query databases using natural language. Our approach automatically translates natural language queries intosql, ensuring both syntactic and semantic correctness, while also generating clear, natural language responses from the retrieved data. By streamlining the interaction between users and databases, this method empowers individuals without technical expertise to engage with data directly and efficiently, democratizing access to valuable insights and enhancing productivity.
  arXiv  Detail & Related papers  (2024-09-23T01:07:02Z)
• Text2SQL is Not Enough: Unifying AI and Databases with TAG [47.45480855418987]
  Table-Augmented Generation (TAG) is a paradigm for answering natural language questions over databases. We develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly.
  arXiv  Detail & Related papers  (2024-08-27T00:50:14Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Learning Phonotactics from Linguistic Informants [54.086544221761486]
  Our model iteratively selects or synthesizes a data-point according to one of a range of information-theoretic policies. We find that the information-theoretic policies that our model uses to select items to query the informant achieve sample efficiency comparable to, or greater than, fully supervised approaches.
  arXiv  Detail & Related papers  (2024-05-08T00:18:56Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language [0.0]
  We propose a semantic parsing approach to address the challenge of converting complex natural language into SQL. We modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production. To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
  arXiv  Detail & Related papers  (2022-10-16T13:31:32Z)
• xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph [0.17188280334580192]
  Translating natural language queries into structured query language (NLQ) in interfaces to relational databases is a challenging task. We propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.
  arXiv  Detail & Related papers  (2022-10-07T18:17:09Z)
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions [102.8606542189429]
  The goal of text-to-corpora parsing is to convert a natural language (NL) question to its corresponding structured query language () based on the evidences provided by databases. Deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output query.
  arXiv  Detail & Related papers  (2022-08-29T14:24:13Z)
• Translating synthetic natural language to database queries: a polyglot deep learning framework [0.0]
  Polyglotter supports the mapping of natural language searches to database queries. It does not require the creation of manually annotated data for training. Our results indicate that our framework performs well on both synthetic and real databases.
  arXiv  Detail & Related papers  (2021-04-14T17:43:51Z)
• Towards a Natural Language Query Processing System [0.0]
  This paper reports our study on the design and development of a natural language query interface to a backend relational database. The novelty in the study lies in defining a graph database as a middle layer to store necessary metadata needed to transform a natural language query into structured query language. The translation results for some sample queries yielded a 90% accuracy rate.
  arXiv  Detail & Related papers  (2020-09-25T19:52:20Z)
• DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis [71.40586258509394]
  We propose DomBERT, an extension of BERT to learn from both in-domain corpus and relevant domain corpora. Experiments are conducted on an assortment of tasks in aspect-based sentiment analysis, demonstrating promising results.
  arXiv  Detail & Related papers  (2020-04-28T21:07:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.