Abstract
Knowledge that quantifies the similarity between complex objects forms a vital part of problem-solving expertise within several knowledge- intensive tasks. This paper shows how implicit knowledge about object similari- ties is made explicit in the form of a similarity measure.
The development of a similarity measure is highly domain-dependent. We will use the domain of fluidic engineering as a complex and realistic platform to present our ideas. The evaluation of the similarity between two fluidic circuits is needed for several tasks: (i) Design problems can be suppported by retrievimg an existing circuit which resembles an (incomplete) circuit description. (ii) The problem of visualizing technical documents can be reduced to the problem of arranging similar documents with respect to their similarity.
The paper in hand presents new approaches for the construction of a similarity function: Based on knowledge sources that allow for an expert-friendly knowl- edge acquisition, machine learning is used to compute an explicit similarity func- tion from the acquainted knowledge.
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Stein, B., Niggemann, O. (2001). Generation of Similarity Measures from Different Sources. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_23
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DOI: https://doi.org/10.1007/3-540-45517-5_23
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