Component Retrieval Using Knowledge-Intensive Conversational CBR

  • Mingyang Gu
  • Ketil Bø
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


One difficulty in software component retrieval comes from users’ incapability to well define their queries. In this paper, we propose a conversational component retrieval model (CCRM) to alleviate this difficulty. CCRM uses a knowledge-intensive conversational case-based reasoning method to help users to construct their queries incrementally through a mixed-initiative question-answering process. In this model, general domain knowledge is captured and utilized in helping tackle the following five tasks: feature inferencing, semantic similarity calculation, integrated question ranking, consistent question clustering and coherent question sequencing. This model is implemented, and evaluated in an image processing component retrieval application. The evaluation result gives us positive support.


Semantic Relation Component Reuse Abductive Inference Feature Inferencing Explanation Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mingyang Gu
    • 1
  • Ketil Bø
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

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