scholarly journals Graph-Based Semantic Web Service Composition for Healthcare Data Integration

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ngamnij Arch-int ◽  
Somjit Arch-int ◽  
Suphachoke Sonsilphong ◽  
Paweena Wanchai
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Data Integration ◽  
Large Scale ◽  
Dependency Graph ◽  
Web Services Composition ◽  
Related Services ◽  
Healthcare Data ◽  
Services Composition ◽  
Run Time

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.

An Approach for Biological Data Integration and Knowledge Retrieval Based on Ontology, Semantic Web Services Composition, and AI Planning

2013 ◽  
pp. 1727-1744
Author(s):  
Muhammad Akmal Remli ◽  
Safaai Deris
Keyword(s):  
Artificial Intelligence ◽  
Web Services ◽  
Semantic Web ◽  
Data Integration ◽  
Semantic Web Services ◽  
Ai Planning ◽  
Web Services Composition ◽  
Knowledge Retrieval ◽  
Services Composition ◽  
New Perspective

This chapter describes an approach involved in two knowledge management processes in biological fields, namely data integration and knowledge retrieval based on ontology, Web services, and Artificial Intelligence (AI) planning. For the data integration, Semantic Web combining with ontology is promising several ways to integrate a heterogeneous biological database. The goal of this work is to construct an integration approach for gram-positive bacteria organism that combines gene, protein, and pathway, thus allowing biological questions to be answered. The authors present a new perspective to retrieve knowledge by using Semantic Web services composition and Artificial Intelligence (AI) planning system, Simple Hierarchical Order Planner 2 (SHOP2). A Semantic Web service annotated with domain ontology is used to describe services for biological pathway knowledge retrieval at Kyoto Encyclopedia of Gene and Genomes (KEGG) database. The authors investigate the effectiveness of this approach by applying a real world scenario in pathway information retrieval for an organism where the biologist needs to discover the pathway description from a given specific gene of interest. Both of these two processes (data integration and knowledge retrieval) used ontology as the key role to achieve the biological goals.

An Approach for Biological Data Integration and Knowledge Retrieval based on Ontology, Semantic Web Services Composition, and AI Planning

2013 ◽  
pp. 324-342
Author(s):  
Muhammad Akmal Remli ◽  
Safaai Deris
Keyword(s):  
Artificial Intelligence ◽  
Web Services ◽  
Semantic Web ◽  
Data Integration ◽  
Semantic Web Services ◽  
Ai Planning ◽  
Web Services Composition ◽  
Knowledge Retrieval ◽  
Services Composition ◽  
New Perspective

This chapter describes an approach involved in two knowledge management processes in biological fields, namely data integration and knowledge retrieval based on ontology, Web services, and Artificial Intelligence (AI) planning. For the data integration, Semantic Web combining with ontology is promising several ways to integrate a heterogeneous biological database. The goal of this work is to construct an integration approach for gram-positive bacteria organism that combines gene, protein, and pathway, thus allowing biological questions to be answered. The authors present a new perspective to retrieve knowledge by using Semantic Web services composition and Artificial Intelligence (AI) planning system, Simple Hierarchical Order Planner 2 (SHOP2). A Semantic Web service annotated with domain ontology is used to describe services for biological pathway knowledge retrieval at Kyoto Encyclopedia of Gene and Genomes (KEGG) database. The authors investigate the effectiveness of this approach by applying a real world scenario in pathway information retrieval for an organism where the biologist needs to discover the pathway description from a given specific gene of interest. Both of these two processes (data integration and knowledge retrieval) used ontology as the key role to achieve the biological goals.

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