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22
Learning to Match Ontologies on the Semantic Web
, 2003
"... On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, errorprone, and clearly not possible at the Web scale. Hence, th ..."
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Cited by 94 (2 self)
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On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, errorprone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give wellfounded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of welldefined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several realworld domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.
Fast hierarchical importance sampling with blue noise properties
 ACM TRANSACTIONS ON GRAPHICS
, 2004
"... This paper presents a novel method for efficiently generating a good sampling pattern given an importance density over a 2D domain. A Penrose tiling is hierarchically subdivided creating a sufficiently large number of sample points. These points are numbered using the Fibonacci number system, and th ..."
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Cited by 75 (8 self)
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This paper presents a novel method for efficiently generating a good sampling pattern given an importance density over a 2D domain. A Penrose tiling is hierarchically subdivided creating a sufficiently large number of sample points. These points are numbered using the Fibonacci number system, and these numbers are used to threshold the samples against the local value of the importance density. Precomputed correction vectors, obtained using relaxation, are used to improve the spectral characteristics of the sampling pattern. The technique is deterministic and very fast; the sampling time grows linearly with the required number of samples. We illustrate our technique with importancebased environment mapping, but the technique is versatile enough to be used in a large variety of computer graphics applications, such as light transport calculations, digital halftoning, geometry processing, and various rendering techniques.
Learning compatibility coefficients for relaxation labeling processes
 IEEE Trans. Pattern Anal. Machine Intell
, 1994
"... AbstractRelaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation o ..."
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Cited by 39 (5 self)
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AbstractRelaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of “compatibility coefficients. ” The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statisticalbased methods have been suggested. In this paper, we propose a rather different viewpoint to solve this problem: we derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach. Index Terms Compatibility coefficients, constraint satisfaction, gradient projection, learning, neural networks, nonlinear
The Dynamics of Nonlinear Relaxation Labeling Processes
, 1997
"... We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symm ..."
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Cited by 31 (10 self)
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We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symmetry condition is met, the algorithm possesses a Liapunov function which turns out to be (the negative of) a wellknown consistency measure. This follows almost immediately from a powerful result of Baum and Eagon developed in the context of Markov chain theory. Moreover, it is seen that most of the essential dynamical properties of the algorithm are retained when the symmetry restriction is relaxed. These properties are also shown to naturally generalize to higherorder relaxation schemes. Some applications and implications of the presented results are finally outlined.
Learning to Map between Structured Representations of Data
, 2002
"... This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include "element location of one representation maps to el ..."
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Cited by 28 (3 self)
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This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include "element location of one representation maps to element address of the other", "contactphone maps to agentphone", and "listedprice maps to price * (1 + taxrate)"...
Efficient generation of poissondisk sampling patterns
 Journal of Graphics Tools
"... Poisson Disk sampling patterns are of interest to the graphics community because their bluenoise properties are desirable in sampling patterns for rendering, illumination, and other computations. Until now, such patterns could only be generated by slow methods with poor convergence, or could only b ..."
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Cited by 14 (0 self)
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Poisson Disk sampling patterns are of interest to the graphics community because their bluenoise properties are desirable in sampling patterns for rendering, illumination, and other computations. Until now, such patterns could only be generated by slow methods with poor convergence, or could only be approximated by jittering individual samples or tiling sets of samples. We present a simple and efficient randomized algorithm for generating true Poisson Disk sampling patterns in a square domain, given a minimum radius R between samples. The algorithm runs in O(N log N) time for a pattern of N points. The method is based on the Voronoi diagram. Source code is available online. 1
POS Tagging Using Relaxation Labelling
 PROCEEDINGS OF 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, COLING
, 1996
"... Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies  to the maximum possible degree  a set of given constraints. This pat)er scribes some experiment ..."
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Cited by 12 (5 self)
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Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies  to the maximum possible degree  a set of given constraints. This pat)er scribes some experiments performed applying it to POS tagging, and the results obtained. it also ponders the possibility of applying it, to Word Sense Disambiguation.
A Machine Learning Approach to POS Tagging
, 1998
"... We have applied the inductive learning of statistical decision trees and relaxation labelling to the Natural Language Processing (nlp) task of morphosyntactic disambiguation (Part Of Speech Tagging). The learning process is supervised and obtains a language model oriented to resolve pos ambiguities, ..."
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Cited by 11 (1 self)
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We have applied the inductive learning of statistical decision trees and relaxation labelling to the Natural Language Processing (nlp) task of morphosyntactic disambiguation (Part Of Speech Tagging). The learning process is supervised and obtains a language model oriented to resolve pos ambiguities, consisting of a set of statistical decision trees expressing distribution of tags and words in some relevant contexts. The acquired decision trees have been directly used in a tagger that is both relatively simple and fast, and which has been tested and evaluated on the Wall Street Journal (wsj) corpus with remarkable accuracy. However, better results can be obtained by translating the trees into rules to feed a flexible relaxation labelling based tagger. In this direction we describe a tagger which is able to use information of any kind (ngrams, automatically acquired constraints, linguistically motivated manually written constraints, etc.), and in particular to incorporate the machine...