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Automated service selection using natural language processing

conference contribution
posted on 2015-01-01, 00:00 authored by Muneera Bano, A Ferrari, D Zowghi, V Gervasi, S Gnesi
© Springer-Verlag Berlin Heidelberg 2015. With the huge number of services that are available online, requirements analysts face an overload of choice when they have to select the most suitable service that satisfies a set of customer requirements. Both service descriptions and requirements are often expressed in natural language (NL), and natural language processing (NLP) tools that can match requirements and service descriptions, while filtering out irrelevant options, might alleviate the problem of choice overload faced by analysts. In this paper, we propose a NLP approach based on Knowledge Graphs that automates the process of service selection by ranking the service descriptions depending on their NL similarity with the requirements. To evaluate the approach, we have performed an experiment with 28 customer requirements and 91 service descriptions, previously ranked by a human assessor. We selected the top-15 services, which were ranked with the proposed approach, and found 53% similar results with respect to top-15 services of the manual ranking. The same task, performed with the traditional cosine similarity ranking, produces only 13% similar results. The outcomes of our experiment are promising, and new insights have also emerged for further improvement of the proposed technique.

History

Event

Requirements Engineering in the Big Data Era. Symposium (2nd : 2015 : Wuhan, China)

Volume

558

Series

Communications in Computer and Information Science

Pagination

3 - 17

Publisher

Springer

Location

Wuhan, China

Place of publication

Cham, Switzerland

Start date

2015-10-18

End date

2015-10-20

ISSN

1865-0929

ISBN-13

9783662486337

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

APRES 2015 : Proceedings of the Second Asia Pacific Symposium Requirements Engineering in the Big Data Era