Extracting Co-Occurrence Relations from ZDDs

Extracting Co-Occurrence Relations from ZDDs

A zero-suppressed binary decision diagram (ZDD) is a graph representation suitable for handling sparse set families. Given a ZDD representing a set family, we present an efficient algorithm to discover a hidden structure, called a co-occurrence relation, on the ground set. This computation can be done in time complexity that is related not to the number of sets, but to some feature values of th...

Different Approaches for Extracting Information from the Co-Occurrence Matrix

In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard appro...

Automatic Annotation of Co-Occurrence Relations

We introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within the graph. Starting point of this work is a graph based on word co-occurrence of the German language, which is created by applying iterated co-occurrence analysis. The edges of the graph have been partially annotated b...

Extracting Relations from XML Documents

XML is becoming a prevalent format for data exchange. Many XML documents have complex schemas that are not always known, and can vary widely between information sources and applications. In contrast, database applications rely mainly on the flat relational model. We propose a novel, partially supervised approach for extracting userdefined relations from XML documents with unknown schema. The ex...

Extracting Relations from Unstructured Text