18,584 research outputs found
SQL Query Completion for Data Exploration
Within the big data tsunami, relational databases and SQL are still there and
remain mandatory in most of cases for accessing data. On the one hand, SQL is
easy-to-use by non specialists and allows to identify pertinent initial data at
the very beginning of the data exploration process. On the other hand, it is
not always so easy to formulate SQL queries: nowadays, it is more and more
frequent to have several databases available for one application domain, some
of them with hundreds of tables and/or attributes. Identifying the pertinent
conditions to select the desired data, or even identifying relevant attributes
is far from trivial. To make it easier to write SQL queries, we propose the
notion of SQL query completion: given a query, it suggests additional
conditions to be added to its WHERE clause. This completion is semantic, as it
relies on the data from the database, unlike current completion tools that are
mostly syntactic. Since the process can be repeated over and over again --
until the data analyst reaches her data of interest --, SQL query completion
facilitates the exploration of databases. SQL query completion has been
implemented in a SQL editor on top of a database management system. For the
evaluation, two questions need to be studied: first, does the completion speed
up the writing of SQL queries? Second , is the completion easily adopted by
users? A thorough experiment has been conducted on a group of 70 computer
science students divided in two groups (one with the completion and the other
one without) to answer those questions. The results are positive and very
promising
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Sequence to Sequence Learning for Query Expansion
Using sequence to sequence algorithms for query expansion has not been
explored yet in Information Retrieval literature nor in Question-Answering's.
We tried to fill this gap in the literature with a custom Query Expansion
engine trained and tested on open datasets. Starting from open datasets, we
built a Query Expansion training set using sentence-embeddings-based Keyword
Extraction. We therefore assessed the ability of the Sequence to Sequence
neural networks to capture expanding relations in the words embeddings' space.Comment: 8 pages, 2 figures, AAAI-19 Student Abstract and Poster Progra
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