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An Approach to Mine Business Rule Intents from Domain-specific Documents

Authors:

Abhidip Bhattacharyya,

Pavan Kumar Chittimalli,

Ravindra NaikAuthors Info & Claims

ISEC '17: Proceedings of the 10th Innovations in Software Engineering Conference

Pages 96 - 106

https://doi.org/10.1145/3021460.3021470

Published: 05 February 2017 Publication History

Abstract

An enterprise system enables business by providing various services that are guided by set of well-defined processes, and adhere to certain business rules and constraints. The business rules are usually written using English in operating procedures, terms and conditions, and various other supporting documents. For implementing the business rules in a software system, or expressing them as UML use-case specifications, analysts manually interpret the documents, leading to potential discrepancies, ambiguities, and quality issues in the software system that can be resolved only after testing.

To minimize such errors, we propose a novel method to mine the documents automatically to extract the fundamental atomic facts in every sentence - called as business rule intents. We adopt dependency tree parser to parse the rule sentences and extract rule intents from them. Our experiments using few publicly available sample documents in the financial domain yielded very promising results, where rule intents extraction produced an average precision of 78% and recall of 80%.

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Cited By

Prakash CChittimalli PNaik R(2021)Open Information Extraction Using Dependency Parser for Business Rule Mining in SBVR FormatProceedings of the 14th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3452383.3452396(1-11)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1145/3452383.3452396
Chittimalli PPrakash CNaik RBhattacharyya AJain SGupta ALo DSaha DSharma R(2020)An Approach to Mine SBVR Vocabularies and Rules from Business DocumentsProceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3385032.3385046(1-11)Online publication date: 27-Feb-2020
https://dl.acm.org/doi/10.1145/3385032.3385046
Chittimalli PBhattacharyya ANaik RSarkar SHildebrandt TKumar ASharma R(2019)SBVR-based Business Rule Creation for Legacy Programs using Variable ProvenanceProceedings of the 12th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3299771.3299786(1-11)Online publication date: 14-Feb-2019
https://dl.acm.org/doi/10.1145/3299771.3299786
Show More Cited By

An Approach to Mine Business Rule Intents from Domain-specific Documents
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Other conferences

ISEC '17: Proceedings of the 10th Innovations in Software Engineering Conference

February 2017

235 pages

ISBN:9781450348560

DOI:10.1145/3021460

General Chair:
Ravi Prakash Gorthi
LNMIIT, Jaipur
,
Program Chairs:
Santonu Sarkar
BITS Pilani, K.K.Birla Goa Campus
,
Nenad Medvidovic
University of Southern California, LA, USA
,
Vinay Kulkarni
TCS, Pune
,
Atul Kumar
IBM Research, Banagalore
,
Padmaja Joshi
C-DAC, Mumbai
,
Paola Inverardi
University of L'Aquila, Italy
,
Publications Chairs:
Ashish Sureka
ABB Corporate Research Center, India
,
Richa Sharma
BML Munjal University, India

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 05 February 2017

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ISEC '17

ISEC '17: Innovations in Software Engineering Conference

February 5 - 7, 2017

Jaipur, India

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ISEC '17 Paper Acceptance Rate 25 of 81 submissions, 31%;

Overall Acceptance Rate 76 of 315 submissions, 24%

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Cited By

Prakash CChittimalli PNaik R(2021)Open Information Extraction Using Dependency Parser for Business Rule Mining in SBVR FormatProceedings of the 14th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3452383.3452396(1-11)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1145/3452383.3452396
Chittimalli PPrakash CNaik RBhattacharyya AJain SGupta ALo DSaha DSharma R(2020)An Approach to Mine SBVR Vocabularies and Rules from Business DocumentsProceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3385032.3385046(1-11)Online publication date: 27-Feb-2020
https://dl.acm.org/doi/10.1145/3385032.3385046
Chittimalli PBhattacharyya ANaik RSarkar SHildebrandt TKumar ASharma R(2019)SBVR-based Business Rule Creation for Legacy Programs using Variable ProvenanceProceedings of the 12th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3299771.3299786(1-11)Online publication date: 14-Feb-2019
https://dl.acm.org/doi/10.1145/3299771.3299786
Chittimalli PAnand KPradhan SMitra SPrakash CShere RNaik RZimmermann TLawall JMarinov D(2019)BuRRiToProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00134(1190-1193)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00134
Bhattacharyya AChittimalli PNaik RReddy YVarma VCleland JBellur USengupta SSharma NLoganathan RSharma RSarkar S(2018)Relation Identification in Business Rules for Domain-specific DocumentsProceedings of the 11th Innovations in Software Engineering Conference10.1145/3172871.3172884(1-5)Online publication date: 9-Feb-2018
https://dl.acm.org/doi/10.1145/3172871.3172884

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