The increasing demand for accessing heterogeneous information sources to support global applications and decision-making requirements forces organizations to solve heterogeneity problems. One of the important problems stemming from accessing the heterogeneous data is semantic heterogeneity. A number of research efforts have been proposed to address this problem, ranging from mediator-based systems, description logic-based systems to content-descriptive metadata systems. In this paper, we propose a metadata dictionary as an assistant mechanism for solving semantic heterogeneity. The proposed metadata dictionary is designed based on domain ontology where the constituent components are defined in terms of object-oriented and set theory. An XML-based data model is employed to manipulate and express the metadata dictionary contents. The inherent flexibility of XML technology permits systemwide interoperability suitable for a web-based environment.

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user’s query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

Ontology Mapping and Rule-Based Inference for Learning Resource Integration Conflict resolution;Learning resources;Ontology mapping;Rule-based inference; With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.