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_5 , the authors focused on integrating uncertainty when matching ontologies. The proposed method modeled and combined the outputs of three ontology matchers. For an entity e @math , the frame of discernment @math is composed of mappings between e and all the concepts in an ontology @math . The different similarity values obtained through the application of the three matchers are interpreted as mass values. Then, a combination of the results of the three matchers is performed.
@cite_5
1500155021 1984093092 2152525669 2612133093
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. 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. This paper proposes a new approach for Cross Modal Matching, i.e. the matching of patterns represented in di erent modalities, when pairs of same di erent data are available for training (e.g. faces of same di erent persons). In this situation, standard approaches such as Partial Least Squares (PLS) or Canonical Correlation Analysis (CCA), map the data into a common latent space that maximizes the covariance, using the information brought by positive pairs only. Our contribution is a new metric learning algorithm, which alleviates this limitation by considering both positive and negative constraints and use them effi ciently to learn a discriminative latent space. The contribution is validated on several datasets for which the proposed approach consistently outperforms PLS CCA as well as more recent discriminative approaches.