# Natural Language Querying System Through Entity Enrichment

arXiv: https://arxiv.org/abs/2410.15753 · HTML: https://mlsanity.com/paper/2410.15753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 45 of its 515 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 30 of 45)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Rasa: Open Source Language Understanding and Dialogue Management](https://mlsanity.com/paper/1712.05181/) (2017)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors](https://mlsanity.com/paper/1606.05893/) (2016)
- [Recurrent neural networks for aortic image sequence segmentation with sparse annotations](https://mlsanity.com/paper/1808.00273/) (2018)
- [Measuring the Similarity of Sentential Arguments in Dialog](https://mlsanity.com/paper/1709.01887/) (2017)
- [FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging](https://mlsanity.com/paper/1909.11946/) (2019)
- [OpenCitations, an infrastructure organization for open scholarship](https://mlsanity.com/paper/1906.11964/) (2019)
- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning](https://mlsanity.com/paper/1611.06224/) (2016)
- [BINet: Multi-perspective Business Process Anomaly Classification](https://mlsanity.com/paper/1902.03155/) (2019)
- [Machine Learning at Microsoft with ML .NET](https://mlsanity.com/paper/1905.05715/) (2019)
- [Knapsack Voting for Participatory Budgeting](https://mlsanity.com/paper/2009.06856/) (2020)
- ['Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators](https://mlsanity.com/paper/1402.7035/) (2014)
- [Extracting Multiple Viewpoint Models from Relational Databases](https://mlsanity.com/paper/2001.02562/) (2020)
- [Large-Scale Evaluation of Keyphrase Extraction Models](https://mlsanity.com/paper/2003.04628/) (2020)
- [Analyzing Business Process Anomalies Using Autoencoders](https://mlsanity.com/paper/1803.01092/) (2018)

## Abstract (arXiv)

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.