Enabling Natural Language Analytics over Relational Data using Formal Concept Analysis
C. Anantaram, Mouli Rastogi, Mrinal Rawat, Pratik Saini
Journal of Information & Systems Management, Vol. 10, No. 2, 2020.
DOI: https://doi.org/10.6025/jism/2020/10/2/33-41
Link: https://www.dline.info/jism/fulltext/v10n2/jismv10n2_1.pdf
Abstract:
Analysts like to pose a variety of questions over large relational databases containing data on the domain that they are analyzing. Enabling natural language question answering over such data for analysts requires mechanisms to extract exceptions in data, find steps to transform data, detect implications in the data, and apply classifications on the data. Motivated by this problem, we propose a semantically enriched deep learning pipeline that supports natural language question answering over relational databases and uses Formal Concept Analysis to find exceptions, classification and
transformation steps. Our frame- work is based on a set of deep learning sequence tagging networks which extracts information from the NL sentence and constructs an equivalent intermediate sketch, and then maps it into the actual tables and
columns of the database. The output data of the query is converted into a lattice structure which results into the (extent,
intent) tuples. These tuples are then analyzed to find the exceptions, classification and transformation steps.