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One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the
How to find the teaching resources according to users' demand quickly and accurately on the Internet is urgent to be solved. This paper proposes a design of pretreatment for keyword-based search over network teaching resource database based on ontology. Firstly, the teaching ontology is created according to the
suggested in this study. Four keyword-based research networks, with journal paper or research project as network actors, constructed previously are selected as the targets of this empirical study: 1) Technology Foresight Paper Network: 181 papers and 547 keywords, 2) Regional Innovation System Paper Network: 431 papers and
more accurately and simply. RDF is a language for representing metadata. An enormous number of keywords on the semantic Web are very important to make practical applications of the semantic Web because most users prefer to search with keywords. In this paper, we classify queries with keyword conditions into three patterns
Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer
Webpage keyword is widely used in personalized search and recommendations. The accuracy of keyword is significant to the quality of search and recommendation result. Current keyword extraction methods' accuracy is not high. In order to make up for the shortage of present technology, a new keyword weight adjusting
suggest the ways that make and renew the ontology, which are related with the keywords that users input in the search engine, automatically for the automatic generation of ontology that is not limited by specific domain. Input keyword and relation keywords become OWL, and the relation among the created OWL is expressed by
that assists to both service producers and service consumers in the discovery of semantic keywords which can be used to describe and discover Web services respectively. First, our system enhances semantically the list of keywords extracted from the elements that comprise the description of a Web service and the user
Matching search technology based on query keyword has been widely used by traditional search way. It still belongs to pure keyword matching and can not acquire satisfactory search results. The essential reason is that traditional Web search lacks semantic understanding to user's search behaviors. In this study, we
During the last several years, the volume of user- generated content on the web has skyrocketed. Today, the major Internet players such as MySpace, Wordpress, and YouTube all provide a variety of Web 2.0 based applications that allow users to post photos, share video, and manage blogs with multimedia content. Keyword
Web ontologies provide shared concepts for describing domain entities and thus enable semantic interoperability between applications. To facilitate concept sharing and ontology reusing, we developed Falcons Concept Search, a novel keyword-based ontology search engine. In this paper, we illustrate how the proposed mode
In the Web 2.0 Age, Web bloggers have become major Web content providers, and the volume of blogs continues to increase rapidly. Although keyword-based tags are widely used to classify submitted blogs and to search for retrieval, this keyword-based approach provides less optimal classification, leading to less
There are hundreds of millions of images available on the current World Wide Web. The demand for image retrieval online is growing dramatically. For multimedia documents, the typical keyword-based retrieval method has encountered problems mainly in the areas of: 1) the quality of the search result; 2) the usage of the
? Doesn't it understand it now? Google is very good at correcting typing mistakes, figuring out what I “meant” when I miss-typed a query or suggesting keyword to expand our search query. In this paper we redefine the idea of searching optimised information on the web using Capture-Recapture method. This paper
conceptual model is well defined, a set of rules for keyword searching is created to verify preciseness of output produced. The rules created in this paper will be executed on Herbal Research E-Centre prototype.
With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldnpsilat make users satisfied. This paper presents
Metacat to improve metadata search in multiple ways: (i) by expanding standard keyword searches with ontology term hierarchies; (ii) by allowing keyword searches to be applied to annotations in addition to traditional meta-data; and (iii) by allowing more structured searches over annotations via ontology terms. We describe
With the advent of Web 2.0, RESTful web services are becoming increasingly popular to emphasize the web as platform. There are already many RESTful web services and the number of services is increasing rapidly. Thus, it can be difficult to find specific services using keyword based retrieval. To solve this problem, a
The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users
Due to the explosive growth of the amount of Web information, the effectiveness of keyword-based searching methods appears to reach a limit. One major reason is that the mixture of content and presentation information hinders machines in understanding the context of Web information and as a result, the performance of
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