The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. The goal of ontology integration is to merge two or more given ontologies in order to provide a unified view on the input ontologies while maintaining all information coming from them. We propose a new taxonomy merging algorithm that, given as input two taxonomies and...

In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm (called ProbaMap) which minimizes the number of calls to the probability estimator ...

We present an algorithm that automates the process of identifying implementation-based testing techniques that are suitable for testing a given class. The algorithm accepts a summary of the class under test and a set representing testing techniques available to the developer engaged in performing the test. The summary of the class is based on our taxonomy that maps the characteristics of a clas...

In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically starting from a domain corpus and the Web. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions and hypernyms. This results in a very dense, cyclic and possibly di...

Although many algorithms have been developed to harvest lexical resources, few organize the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a root concept, a basic level concept, and recursive surface patterns to learn automatically from the Web hyponym-hypernym pairs subordinated to the root; (2) a Web based concept positioning procedure to validate the learne...

This report addresses the problem of learning a taxonomy from a given domain-specific text corpus. We propose a novel unsupervised algorithm for this problem. Its key contributions include a clustering-based inference approach that increases recall over surface patterns and a graph-based algorithm for detecting incorrect edges that improves precision. Our system induces the taxonomy simply by a...

In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community.1 In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn...