This paper proposes an alternative to formal annotation for the representation of semantics, and presents an
extension to it capable of handling multimedia (text and images) documents. The article argues that meaning
is not a property of a document, but an outcome of a contextualized and situated process of interpretation. The
consequence of this position is that one should not quite try to represent the meaning of a document (the way
formal annotation does), but the context of the activity of which search is part.
We present some general considerations on the representation and use of the context, and a simple example
of a technique to encode the context represented by the documents collected in the computer in which one
is working, and to use them to direct search. We show preliminary results showing that even this rather
simpleminded context representation can lead to considerable improvements with respect to commercial search
engines both for text and images.
A fuzzy logic-based system to classify olfactive signals is presented. The odor samples are obtained from an electronic noise that contains conducting polymer sensors with partially overlapping sensitivities to odors. The sensor responses are represented by means of the coefficients of their Fast Fourier Transform (FFT). A feature reduction method is applied to reduce the feature space dimension. Then, an unsupervised Fuzzy Divisive Hierarchical Clustering (FDHC) method is used to establish the optimal number of clusters in the data set as well as the optimal cluster structure. The output of FDHC is a binary hierarchy of fuzzy classes that are used to build a supervised fuzzy hierarchical classifier. At each level of the fuzzy hierarchy a separating hyperplane of the two corresponding fuzzy training classes is determined. The hyperplane identifies two crisp decision regions, which will be refined at the next level of the hierarchy. In this way, we obtain a hierarchy of regions, which defines a crisp decision tree. Each region is, therefore, related to a specific expected output of the system. Recognition of an unknown odor is accomplished by computing the FFT of the corresponding signal and using the decision tree to establish the region the odor belongs to. Two small-scale applications of the method yielded 100% classification accuracy on out-of-sample data.
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