Innovative Semantic Web Services for Next Generation Academic Electronic Library via Web 3.0 via Distributed Artificial Intelligence

2012 ◽  
pp. 118-124 ◽  
Author(s):  
Hai-Cheng Chu ◽  
Szu-Wei Yang
Keyword(s):  
Artificial Intelligence ◽  
Web Services ◽  
Semantic Web ◽  
Distributed Artificial Intelligence ◽  
Semantic Web Services ◽  
Next Generation ◽  
Electronic Library ◽  
Web 3.0

A New Framework for Intelligent Semantic Web Services Based on GAIVAs

2010 ◽  
pp. 38-62
Author(s):  
Iglesias Andrés
Keyword(s):  
Artificial Intelligence ◽  
User Interface ◽  
Web Services ◽  
Semantic Web ◽  
Semantic Web Services ◽  
Virtual Agents ◽  
Web Pages ◽  
Artificial Intelligence Techniques ◽  
Intelligent Virtual Agents ◽  
New Framework

The Semantic Web has been recently developed to provide end users with suitable tools and strategies to process information from their Web pages. The Intelligent Semantic Web Services is a new approach aimed at extending Semantic Web capabilities for Services by applying Artificial Intelligence techniques while maintaining the good properties of the standard Semantic Web schemes. However, many current Web services neither consider this approach nor include a powerful user-interface and, consequently, are very limited and difficult to use. This paper introduces a new framework for Intelligent Semantic Web Services that overcomes these drawbacks. Our approach is based on the Graphical Autonomous Intelligent Virtual Agents (GAIVAs), virtual agents that exhibit a human-like appearance and behaviour and are able to take intelligent decisions and perform actions without human intervention. To this purpose, the framework comprises a collection of powerful Artificial Intelligence techniques along with a natural and intuitive Graphical User Interface.

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.

PROCESS MODEL ONTOLOGY-BASED MATCHMAKING OF SEMANTIC WEB SERVICES

2011 ◽  
Vol 20 (04) ◽  
pp. 357-370 ◽  
Author(s):  
D. PAULRAJ ◽  
S. SWAMYNATHAN ◽  
M. MADHAIYAN
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
Process Model ◽  
Service Discovery ◽  
Service Oriented Architecture ◽  
Semantic Web Services ◽  
Functional Part ◽  
Service Oriented ◽  
Concise Description

One of the key challenges of the Service Oriented Architecture is the discovery of relevant services for a given task. In Semantic Web Services, service discovery is generally achieved by using the service profile ontology of OWL-S. Profile of a service is a derived, concise description and not a functional part of the semantic web service. There is no schema present in the service profile to describe the input, output (IO), and the IOs in the service profile are not always annotated with ontology concepts, whereas the process model has such a schema to describe the IOs which are always annotated with ontology concepts. In this paper, we propose a complementary sophisticated matchmaking approach which uses the concrete process model ontology of OWL-S instead of the concise service profile ontology. Empirical analysis shows that high precision and recall can be achieved by using the process model-based service discovery.

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