Considering the high heterogeneity of the ontologies published on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology. Perfectible similarity measures, considered as sources of information, are combined to establish these links. The theory of belief functions is a powerful mathematical tool for combining such uncertain information. In this paper, we introduce a decision process based on a distance measure to identify the best possible matching entities for a given source entity.
In @cite_12 , the author viewed ontology matching as a decision making process that must be handled under uncertainty. He presented a generic framework that uses Dempster-Shafer theory as a mathematical model for representing uncertain mappings as well as combining the results of the different matchers. Given two ontologies @math and @math , the frame of discernment represents the Cartesian product e x @math where each hypothesis is the couple @math such as e @math and @math . Each matcher is considered as an expert that returns a similarity measure converted into a basic belief mass. The Dempster rule of combination is used to combine the results provided by a matcher. The pairs with plausibility and belief below a given threshold are discarded. The remaining pairs represent the best mapping for a given entity.
@cite_12
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An ontology matching method (or a matcher) aims at matching every entity (or concept) in one ontology to the most suitable entity (or entities) in another ontology. Usually it is almost impossible to find a perfect match in the second ontology for every entity in the first ontology, so a matcher generally returns a set of possible matches with some weights (uncertainty) attached to each pair of match. In order to improve a matching result, several matchers can be used and the matched results from these matchers are combined with suitable approaches. In this paper, we first propose two new matchers among three matchers we use. We then address the need of dealing with uncertainties in mapping by investigating how some uncertainty reasoning frameworks can be used to combine matching results. We apply both the Dempster Shafer theory of evidence (DS theory) and Possibility Theory to merge the results computed by different matchers. Our experimental results and comparisons with related work indicate that integrating these theories to deal with uncertain ontology matching is a promising way to improve the overall matching results. Ontologies, at least in the form of taxonomies, have proved rather successful, and are employed in many fields, as far apart as biology and finance. Reaching an agreement over a single ontology has proved difficult, and to obtain actual interoperability it is necessary to map the different ontologies. Mapping one entity between a source ontology and one in a target ontology means to compare the first entity with all the entities in the second ontology: matchers analyse different aspects of the entities to identify the similarities. A single matcher can analyse only some aspects, and often has to rely on uncertain information. Therefore combining the outcomes of different matchers can yield better results. In this paper I present a framework that uses Dempster-Shafer as a model for interpreting and combining results computed by the matchers. We present a new method for the robust detection and matching of multiple planes in pairs of images. Such planes can serve as stable landmarks for vision-based urban navigation. Our approach starts from SIFT matches and generates multiple local homography hypotheses using the recent J-linkage technique by Toldo and Fusiello, a robust randomized multi-model estimation algorithm. These hypotheses are then globally merged, spatially analyzed, robustly fitted, and checked for stability. When tested on more than 30,000 image pairs taken from panoramic views of a college campus, our method yields no false positives and recovers 72 of the matchable building walls identified by a human, despite significant occlusions and viewpoint changes. This paper presents a novel and robust approach to consistent labeling for people surveillance in multicamera systems. A general framework scalable to any number of cameras with overlapped views is devised. An offline training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.