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A taxonomy organizes concepts or topics in a hierarchical structure and can be created manually or via automated systems. One major drawback of taxonomies is that they require users to have the same view of the topics as the taxonomy creator. That is, when a user follows a top-down path to find the specific topic of her interest, she has to make ch...
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... As underlined by Bailey (1994), classification is one of the most central and generic of all our conceptual exercises; without it there could be no advanced conceptualization, reasoning, language, data analysis or, for that matter, social science research. Some authors point to limitations of focusing on classifications: for example, to be really useful, users are required to have the same view of the classified topics as the creator (Qi et al., 2010); additionally, it has been pointed out that any classification, while introducing some stability in the interpretation and use of relevant knowledge, should be truly considered as a temporary construction (Pando & De Almeida, 2016). ...
The main goal of the paper is to provide a statistical categorization of small and micro knowledge‐intensive business service (KIBS) companies, based on their knowledge management (KM) attitude. Since knowledge is the main production factor and output of these companies, it is essential to achieve a better understanding of how they manage this resource. A questionnaire‐based survey was conducted on a sample of Polish small and micro KIBS operating in various service sectors. A cluster analysis of the data was performed, to categorize the sample according to the KM attitude of the companies. Three main groups of companies were identified, varying in terms of their levels of “knowledge needs”, “intensity of use” of KM practices and “perceived barriers to KM implementation”. This classification is shown to characterize attitudes towards KM to a higher level of statistical significance than do structural characteristics. The survey was based on a single country sample. On the one hand, this provides consistency to the analysis. On the other hand, further insights can be obtained by a multi‐national study. In addition, cluster analysis is exploratory in nature. The results provide useful insights for policy makers (to formulate policies for facilitating KM implementation in small KIBS) and managers (to reflect on the KM attitudes of their company). The statistical categorization of small and micro KIBS in terms of their KM attitude has been very rarely undertaken. Even the most recent investigations of KM issues used samples from large companies.
... Approaches in the third category use term co-occurrence as the measure for term relatedness, assuming the asymmetric occurrence relationship between terms indicates their semantic subsumptions [8,2]. Our full report [7] on this work con- tains a more comprehensive review of the broad data organization problem and taxonomy generation approaches. ...
... Here, we only report important results because of the length limit. For detailed results and analysis, please refer to our technical report [7]. ...
A taxonomy organizes concepts or topics in a hierarchical structure and can be created manually or via automated systems. A major drawback of taxonomies is that they require users to have the same view of the topics as the taxonomy creator. Users who do not share that mental taxonomy are likely to have difficulty in finding the desired topic. In this paper, we propose a new approach to taxonomy expansion which is able to provide more flexible views. Based on an existing taxonomy, our algorithm finds possible alternative paths and generates an expanded taxonomy with flexibility in user browsing choices. In experiments on the dmoz Open Directory Project, the rebuilt taxonomies provide more alternative paths and shorter paths to information. User studies show that our expanded taxonomies are preferred compared to the original
