Ontology mapping is the process whereby two ontologies are semantically related at conceptual level and the source ontology instances are transformed into target ontology entities according to those semantic relations. The objective of MAFRA–MApping FRAmework – is to cover all the phases of the ontology mapping process, including analysis, specification, representation, execution and evolution....

To explore the potential application of semantic roles in structural machine translation, we propose to study the automatic learning of English-Chinese bilingual predicate argument structure mapping. We describe ARG ALIGN, a new model for learning bilingual semantic frames that employs monolingual Chinese and English semantic parsers to learn bilingual semantic role mappings with 72.45% Fscore,...

OBJECTIVE Word finding depends on the processing of semantic and lexical information, and it involves an intermediate level for mapping semantic-to-lexical information which also subserves lexical-to-semantic mapping during word comprehension. However, the brain regions implementing these components are still controversial and have not been clarified via a comprehensive lesion model encompassin...

The need for integrating geospatial information (GI) data from various heterogeneous sources has seen increased importance for geographic information system (GIS) interoperability. Using domain ontologies to clarify and integrate the semantics of data is considered as a crucial step for successful semantic integration in the GI domain. Nevertheless, mechanisms are still needed to facilitate sem...

There is general agreement that the problem of data semantics has to be addressed for XML data to become machine-processable. This problem can be tackled by defining a semantic mapping between an XML schema and an ontology. Unfortunately, creating such mappings is a tedious, time-consuming, and error-prone task. To alleviate this problem, we present a solution that heuristically discovers seman...

This paper summarizes our recent research works on semantic video search. Our solution to this exciting topic is grounded on concept-based video search, which attempts to narrow the semantic gap by first indexing the video content with a large number of pre-trained semantic concepts, and then employing these concepts to respond user’s queries (query-by-concept). We developed novel and effective...

Every information system has its own domain model for its environment and efficient task operation, which results in diverse heterogeneities, especially the semantic Heterogeneity. In this paper, order to adapt to the data request model of railway system, and semantic conflict type existing in the process of data integration, Ontology technology is employed in heterogeneous information integrat...

Software Engineers are interested in understanding software structure in order to manage its complexity, and to assure and improve its quality. Semantic models can inform tools to communicate this information to humans. The .NET framework provides a common semantic underpinning for a wide range of programming languages in an intermediate language. Nevertheless, there is a semantic gap between t...

Semantic integration became an attractive area of research in several disciplines, such as information integration, databases and ontologies. Huge amount of data is still stored in relational databases (RDBs) that can be used to build ontology, and the database cannot be used directly by the semantic web. Therefore, one of the main challenges of the semantic web is mapping relational databases ...

Current research to harness the power of P2P networks involves building reliable Semantic Peerto-Peer (SP2P) systems. SP2P systems combine two complementary technologies: P2P networking and ontologies. There are several types of SP2P systems with applications to knowledge management systems, databases, the Semantic Web, emergent semantics, web services, and information systems. Correct semantic...