Automated service selection using natural language processing

Bano, M; Ferrari, A; Zowghi, D; Gervasi, V; Gnesi, S

Automated service selection using natural language processing

Bano, M

Ferrari, A Zowghi, D

Gervasi, V Gnesi, S

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Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2015, 558 pp. 3 - 17
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Bano, M https://orcid.org/0000-0002-1447-9521	en_US
dc.contributor.author	Ferrari, A	en_US
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155	en_US
dc.contributor.author	Gervasi, V	en_US
dc.contributor.author	Gnesi, S	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2015, 558 pp. 3 - 17	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37668
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-3-662-48634-4_1	en_US
dc.title	Automated service selection using natural language processing	en_US
dc.type	Conference Proceeding
utslib.citation.volume	558	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/FASS Faculty Administration
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	558	en_US

Abstract:

© 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.

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http://hdl.handle.net/10453/37668