The Evaluation of a Hierarchical Case Representation
Using Context Guided Retrieval

Ian Watson & Srinath Perera*
AI-CBR. Bridgewater Building. University of Salford, Salford, M5 4WT, UK
*Department of Building Economics, University of Moratua, Moratua. Sri Lanka
{i.d.watson | r.s.perera}@surveying.salford.ac.uk

Abstract. This paper presents the results of the comparison of the performance of a
hierarchical case representation using a context guided retrieval method against that of a
simpler flat file representation using standard nearest neighbour retrieval. The estimation of
the construction costs of light industrial warehouse buildings is used as the test domain.
Each case comprises approximately 400 features. These are structured into a hierarchical
case representation that holds more general contextual features at its top and specific
building elements at its leaves. A modified nearest neighbour retrieval algorithm is used
that is guided by contextual similarity. Problems are decomposed into sub-problems and
solutions recomposed into a final solution. The comparative results show that the context
guided retrieval method using the hierarchical case representation out performs the simple
flat file representation and standard nearest neighbour retrieval.

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