Title
Automated extraction of product comparison matrices from informal product descriptions.
Abstract
We propose a procedure for extracting comparison matrices from informal product descriptions.We evaluate our proposal against numerous categories of products mined from BestBuy.Matrices exhibit numerous comparable information and can supplement or even refine technical descriptions.A user study shows that our automated approach retrieves a significant portion of correct information.Users can compute, control, edit and refine matrices in a Web environment called MatrixMiner. Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product.In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM - despite the informality and absence of structure in the textual descriptions of products.We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.
Year
DOI
Venue
2017
10.1016/j.jss.2016.11.018
Journal of Systems and Software
Keywords
Field
DocType
Software product lines,Variability mining,Feature mining,Product comparison matrices,Reverse engineering
Data mining,Technical specifications,Information retrieval,Systems engineering,Computer science,Matrix (mathematics),Reverse engineering,Natural language,Information extraction,Product description,Cluster analysis,Traceability
Journal
Volume
Issue
ISSN
124
C
0164-1212
Citations 
PageRank 
References 
15
0.57
27
Authors
7
Name
Order
Citations
PageRank
Sana Ben Nasr1312.48
Guillaume Bécan2574.30
Mathieu Acher374752.36
João Bosco Ferreira Filho4436.27
Nicolas Sannier5887.77
Benoit Baudry62000118.08
Jean-Marc Davril7563.94