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Semantic Knowledge Management System to Support Software Engineers: Implementation and Static Evaluation through Interviews at Ericsson
Borsa Istanbul, TUR.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2018 (English)In: e-Informatica Software Engineering Journal, ISSN 1897-7979, E-ISSN 2084-4840, Vol. 12, no 1, p. 237-263Article in journal (Refereed) Published
Abstract [en]

Background: In large-scale corporations in the software engineering context information overload problems occur as stakeholders continuously produce useful information on process life-cycle issues, matters related to specific products under development, etc. Information overload makes finding relevant information (e.g., how did the company apply the requirements process for product X?) challenging, which is in the primary focus of this paper. Contribution: In this study the authors aimed at evaluating the ease of implementing a semantic knowledge management system at Ericsson, including the essential components of such systems (such as text processing, ontologies, semantic annotation and semantic search). Thereafter, feedback on the usefulness of the system was collected from practitioners. Method: A single case study was conducted at a development site of Ericsson AB in Sweden. Results: It was found that semantic knowledge management systems are challenging to implement, this refers in particular to the implementation and integration of ontologies. Specific ontologies for structuring and filtering are essential, such as domain ontologies and ontologies distinct to the organization. Conclusion: To be readily adopted and transferable to practice, desired ontologies need to be implemented and integrated into semantic knowledge management frameworks with ease, given that the desired ontologies are dependent on organizations and domains.

Place, publisher, year, edition, pages
WROCLAW UNIV TECHNOLOGY , 2018. Vol. 12, no 1, p. 237-263
Keywords [en]
knowledge management, information overload, case study, semantic web
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-17048DOI: 10.5277/e-Inf180110ISI: 000445123800002OAI: oai:DiVA.org:bth-17048DiVA, id: diva2:1251384
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open access

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2019-01-16Bibliographically approved

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Petersen, Kai

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Citation style
  • apa
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