Abstract
In this paper, we explore the task of automatic construction of concept maps for various knowledge domains. We propose a simple 3-steps algorithm for extraction of key elements of a concept map (nodes and links) from a given collection of domain documents. Our algorithm manipulates a statistical term-document matrix describing how frequently terms occur in documents of the collection. At the first step we decompose this matrix into scores (terms-by-factors) and loadings (factors-by-documents) matrixes using non-negative matrix factorization, wherein each factor represents one topic of the collection. Since the scores matrix specifies the relative contribution of each term to the factors, we can select the most contributing terms and use them as concept map nodes. At the second step we associate selected key terms with the corresponding row-vectors of the term-document matrix and calculate pairwise cosine distances between them. Since the close distances determine the pairs of strongly related key terms, we can select the strongest relations as concept map links. Finally, we construct the resulting concept map as a graph with selected nodes and links. The benefits of our statistical algorithm are its simplicity, efficiency and applicability to any domain, any language and any document collection.
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Nugumanova, A., Mansurova, M., Alimzhanov, E., Zyryanov, D., Apayev, K. (2016). An Automatic Construction of Concept Maps Based on Statistical Text Mining. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds) Data Management Technologies and Applications. DATA 2015. Communications in Computer and Information Science, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-319-30162-4_3
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DOI: https://doi.org/10.1007/978-3-319-30162-4_3
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