Using Semantic Knowledge Management Systems To Overcome Information Overload Problems In Software Engineering
Abstract: Context. Information overload is an increasingly important problem of our age where the amount of data we have is expanding drastically with the use of digital communication. Information retrieval models are developed to help overcoming this problem with computerized tools. Semantic information retrieval, which means retrieving information based on the interpretations of meanings of the words, is one of these models and started to be used commonly to handle large amount of data in the Internet and in enterprises to overcome information overload problems. Objectives. In this study we investigate different information retrieval models for using with knowledge management systems in large-scale organizations from the perspective of software engineers. To this end, we aim at identifying existing issues and needs about information overload and then assessing different solutions against these needs. Afterwards, we analyze the chosen solution, which is semantic search, and define and carry out an implementation process to reflect on it. Finally, the usefulness and feasibility of this type of solutions to overcome the specified information overload problems in software engineering is studied and discussed. Methods. We performed a literature review to extract the existing knowledge, technology, and the problems and solutions in the defined context. Then a case study was conducted at a development site of Ericsson AB in Sweden. Case study involved unstructured and semi-structured interviews for data collection, and an implementation attempt for a simple semantic knowledge management system. Thematic Coding Analysis method is used for qualitative data analysis. Results. We identified 23 codes that are categorized under 8 themes from the opinions of company practitioners about semantic knowledge management systems. They are mainly about the existing problems, arguments for using semantic system for solving them, and suggestions and challenges. Conclusions. We conclude that semantic knowledge management systems have a very high potential to solve information overload problems in software engineering if the necessary measures are taken. We found that the problems are related to search engine and the document structure of the tools; usefulness of semantic system is the capability of ontology based retrieval to filter out irrelevant documents and extract hidden data and people’s skills and interests; and finally the challenge is the necessary endeavor to elicit and satisfy all the needs.
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