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The aim of service discovery is to discover services based on preferences given by service consumers. Many approaches are using keyword based syntactic methods and recent approaches are using semantic Web technology to enhance service discovery. Traditional service discovery mechanism acts like a black box which
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which
search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result
shortlist the results. These popular Web search engines use first generation search service based on ??static keywords??, which require the users to type in the exact keywords. This approach clearly puts the users in a critical situation of guessing the exact keyword. The users may want to define their search by using
In this paper, an intelligent concept based search engine has been presented that can be used as a multilingual platform for different search queries. It retrieves those results pages also which don't have directly the keywords but contains the synonyms or related words. In response to a query for the word “car
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper we
presented. Extensible experiment results demonstrate that annotated web services by our proposed method can more satisfy requirements of service requesters than keyword-based described web services. It can achieve higher service discovery effectiveness.
A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We
Currently, Web of Things is based on keyword matching which is not beneficial to the development regarding Web of Things. Accordingly, "Semantic Web of Things" is proposed. As far as Semantic Web of Things concerned, the information of things should be represented as ontology-based semantic annotation
Abstract-the existing distributed ontology evolutionapproaches are not scaled to dynamic environments likeSemantic service architecture (SSOA). As the SSOA-basedsystem grows in size, the complexity of ontology changemanagement increases, especially if the services ontologies areheterogeneous. In this paper, a novel agent-based ontologyevolution framework is developed for services which consumeontologies...
The existing search engines retrieve information only based on the keywords. The incapability to search on the basis of the relation between the keywords and the user concepts, generates noise and hence, results in irrelevant retrieval. This leads to the idea of performing Semantic information processing by mapping
Most of the current focused crawling approaches perform syntactic matching, that is, they retrieve documents that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or vice-versa
The inability of the present search engines to map the retrieved result set using semantics of the query keywords has been discussed. The present study suggests a framework to improve the mapping of Concept and Context of the query keywords and thereby remove noise from the query. This ensures more relevant and
Exploring the metadata associated with documents in the semantic Web is a way to increase the precision of information retrieval systems. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent
The scale of the social web has integrated users in order to organize shared resources. Users freely associate keywords (tags) to resources. This collection of tags creates a folksonomy. Folksonomy is a collaborative tagging system, which has grown popular with its simplicity of free tagging. However, it rises up a
Traditionally information retrieval consists mainly of determining which documents of a collection contain the keywords in the user query. However, a growing number of tasks, especially those related to Semantic Web technologies and applications rely on accurately measuring the similarity between documents and online
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