Query Formation and Information Retrieval with Ontology

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

Application of Semantic Web Based on the Domain-Specific Ontology for Global KM

This chapter introduces an application of the Semantic Web based on ontology to the tourism business. Tourism business is one promising area for Semantic Web applications. To realize the potential of the Semantic Web, we need to find a killer application of the Semantic Web in the knowledge management (KM) area. The ontology as a key enabler is deigned and implemented under a framework of the Semantic-Web-driven KM system in a tourism domain. Finally, we discussed the relationship between the Semantic Web and KM processes.

A Basis for the Semantic Web and E-Business

This chapter introduces how to effectively organize ontology languages and ontologies and how to efficiently process semantic information based on ontologies. In this chapter we propose the hierarchies to organize ontology languages and ontologies. Based on the hierarchy of ontology languages, the ontology designers need not bear in mind which ontology language the primitives exactly come from, also we can automatically and seamlessly use the ontologies defined with different ontology languages in an integrated environment. Based on the hierarchy of ontologies, the conflicts in different ontologies are resolved, thus the semantics in different ontologies are clear without ambiguities. Also, these semantic-clear ontologies can be used to efficiently process the semantic information in Semantic Web and e-business.

Automatically Extracting and Tagging Business Information for E-Business Systems Using Linguistic Analysis

The Semantic Web will require semantic representations of information that computers can understand when they process business applications. Most Web content is currently represented in formats such as text, that facilitate human understanding, rather than in the more structured formats, that allow automated processing and computer understanding. This chapter explores how natural language processing (NLP) principles, using linguistic analysis, can be employed to extract information from unstructured Web documents and translate it into extensible markup language (XML)—the enabling currency of today’s e-business applications, and the foundation for the emerging Semantic Web languages of tomorrow. Our prototype system is built and tested with online financial documents.

Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare

This chapter will discuss Semantic Web standards and ontologies in two areas: (1) the medical sciences field and (2) the healthcare industry. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems. Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this chapter to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

Utilizing Semantic Web and Software Agents in a Travel Support System

The use of Semantic Web technologies in e-business is hampered by the lack of large, publicly-available sources of semantically-demarcated data. In this chapter, we present a number of intermediate steps on the road toward the Semantic Web. Specifically, we discuss how Semantic Web technologies can be adapted as the centerpiece of an agent-based travel support system. First, we present a complete description of the system under development. Second, we introduce ontologies developed for, and utilized in, our system. Finally, we discuss and illustrate through examples how ontologically demarcated data collected in our system is personalized for individual users. In particular, we show how the proposed ontologies can be used to create, manage, and deploy functional user profiles.

A Communications Model for Knowledge Sharing

An integrative, systems-based model of knowledge sharing can provide a way of visualizing the interrelated elements that comprise a knowledge management system. This original model, building on a rhetorical process model of communication, includes both the objective and subjective elements within the human cognition. In addition, it clarifies the purpose and method elements at the center for any effective knowledge system. The model centers on the purpose elements of intentions and audience, and the method elements of technical tools and human processes. The output of knowledge sharing includes objective products and subjective interpretations. Feedback verifies the timeliness and efficiency in the process of building both information and knowledge.

Ontology Exchange and Integration via Product-Brokering Agents

Agent-based e-commerce has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This chapter addresses the issues of evolving software agents in e-commerce applications. An agent structure with evolutionary features is proposed with a focus on internal hierarchical knowledge. We argue that the knowledge base of an intelligent agent should be the cornerstone for its evolution capabilities, and the agent can enhance its knowledge base by exchanging knowledge with other agents. In this chapter, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration.

Ontology-Based Spelling Correction for Searching Medical Information

There has been a paradigm shift in medical practice. More and more consumers are using the Internet as a source for medical information even before seeing a doctor. The well known fact is that medical terms are often hard to spell. Despite advances in technology, the Internet is still producing futile searches when the search terms are misspelled. Often consumers are frustrated with irrelevant information they retrieve as a result of misspelling. An ontology-based search is one way of assisting users in correcting their spelling errors when searching for medical information. This chapter reviews the types of spelling errors that adults make and identifies current technology available to overcome the problem.

Semantic Knowledge Transparency in E-Business Processes

This chapter introduces a new approach named semantic knowledge transparency, which is defined as the dynamic on-demand and seamless flow of relevant and un-ambiguous, machine-interpretable knowledge resources within organizations and across inter-organizational systems of business partners engaged in collaborative processes. Semantic knowledge transparency is based on extant research in e-business, knowledge management (KM), and the Semantic Web. In addition, theoretical conceptualizations are formalized using description logics (DL) and ontological analysis. As a result, the ontology will support a common vocabulary for transparent knowledge exchange among inter-organizational systems of business partners of a value chain, so that semantic interoperability can be achieved. An example is furnished to illustrate how semantic knowledge transparency in the e-marketplace provides critical input to the supplier discovery and selection decision problem while reducing the transaction and search costs for the buyer organization.