Schema matching is one of the main challenges in different information system integration contexts. Over the past 20 years, different schema matching methods have been proposed and shown to be successful in various situations. Although numerous advanced matching algorithms have emerged, schema matching research remains a critical issue. Different algorithms are implemented to resolve different ...

We present a system for fingerprint verification that approaches the problem as a two-class pattern recognition problem. The features extracted by “FingerCode” are used to capture the ridge strength. This feature vector is then classified as genuine or impostor according to a novel approach to handle the fingerprint verification as a two-class problem. Moreover, we show that extracting the feat...

Nowadays need of increased security in various domains of real life activities demands Automatic Authentication System with fast execution time and accuracy. There are different biometric technologies. But among all biometric technologies, Automatic Fingerprint Matcher System is widely used for person verification and identification. In the Fingerprint Matcher system, different matching methods...

Ontology matching, the task of finding the correspondences that exist between concepts in two different ontologies, is a promising solution to the semantic heterogeneity problem that is faced by the Semantic Web community. This paper describes AgreementMaker, a system for automatically aligning two ontologies. The AgreementMaker system comprises an extensible architecture that allows for the in...

Graph matching is considered the recognition step in computer vision. Our work uses attributed relation graphs (ARG’s) for image representation and analysis, a structural description that we enhance with the complete and coherent spatial knowledge of the source image. In chapter 1 we reveal a trend where researchers are slowly incorporating more and more spatial knowledge into relational graphs...

In this paper we present SEMA tool for the automatic mapping of ontologies. The main purpose of SEMA is to locate one to one equivalence correspondences (mappings) between elements (i.e., classes and properties) of two input ontologies. Towards this goal, SEMA synthesizes lexical, semantic and structural matching algorithms through their iterative execution. 1 Presentation of the system 1.1 Sta...

Schema matching is recognized to be one of the basic operations required by the process of data and schema integration, and thus has a great impact on its outcome. We propose a new approach to combining matchers into ensembles, called Schema Matcher Boosting (SMB). This approach is based on a well-known machine learning technique, called boosting. We present a boosting algorithm for schema matc...