We study an instance-based approach for matching hierarchical ontologies, such as product catalogs. The motivation for utilizing instances is that metadata-based match approaches often suffer from semantic heterogeneity, e.g. ambiguous concept names, different concept granularities or incomparable

that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally-bounded inexact graph match

may or may not be described in terms of their parts) and where models are also described at different levels of abstraction and detail. How-ever, to make decisions we need to be able to reason about what models match particular instances. This paper describes the issues involved in such matching

The notion of similarity between objects nds use in many contexts, e.g., in search engines, collaborative ltering, and clustering. Objects being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do

We propose a feature-based hierarchical framework for hand geometry recognition, based upon matching of geometrical and shape features. Rid of the needs for pegs, the acquisition of the hand images is simplified and more userfriendly. Geometrical significant landmarks are extracted from

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the decomposition of an object category into parts and contours, and discriminatively learn the cost function that drives the matching of the object to the image using Multiple Instance Learning. Using shape-based information, we obtain state-of-the-art localization results on the UIUC and ETHZ datasets.

We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D location and appearance of visual features. Uncertainty in the number of object instances depicted in a particular image

concept in the other one. The framework is based on a model of hierarchical self-organizing maps. Every concept of the two ontologies that are matched is encoded in a bag-of-words style, by counting the words that occur in their OWL concept definition. We evaluated this ontology matching model

We investigate aspects of the performance of the EREW instance of the Hierarchical PRAM (H-PRAM) model, a recursively partitionable PRAM, on the 2D mesh architecture via analysis and simulation experiments. Since one of the ideas behind the H-PRAM is to systematically exploit locality in order