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The growth of the Semantic Web has seen a rapid increase in the amount of Semantic Web data. Meanwhile, the demand for access to Semantic Web data without detailed knowledge of RDF query languages is increasing. In this paper, an approach which can return ranked answers to keyword query without the help of data schema
Semantic and keyword web based technique is becoming a generic issue in an application of Information Retrieval (IR). Most of the researchers used different web techniques for finding relevant information and find the keyword based search, which are not able to fetch the relevant search result because they do not know
In recent years, especially audiovisual media have become the predominant media of the internet. To enable content based video retrieval, high quality textual metadata have to be provided that describe the content. Keyword-based search in general is particularly applicable if the searcher really knows what she is
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 explosive growth of content makes it difficult for end-users to find data that they want. Keyword-based searches are brittle - they require the user to know the set of keywords that the system is using, and are ineffective for finding data based on meaning. This paper describes a new hierarchical overlay model
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
matching of service capabilities using a semantic reasoner. The proposed approach has been evaluated for accuracy using recall and precision. The results are compared with keyword based matching. With a typical test data of 90 services, the proposed method has average recall of 88.2% and average precision of 63.7% in contrast
) Discipline Ontology is constructed, which is the formalization for concepts and the relationships between concepts existing in some discipline domain. OWL is adopted as Discipline Ontology description language; 2) Inference rules are defined on the basis of Discipline Ontology. Semantic extension on keyword from user is
Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We
popularity and co-occurrence data. We describe a prototype that leverages the Wikipedia category structure to allow a user to semantically navigate pages from the Delicious social bookmarking service. In our system a user can perform an ordinary keyword search and browse relevant pages but is also given the ability to broaden
The structure has been put forward of P2P systems are strictly based on the keyword matching to find the way of routing resources, so the routing mechanism will not be able to reflect the massive, distributed resources, and semantic information, thus reducing the precision of service discovery and search prospective
Service-Oriented Computing (SOC) is emerging as a new promising computing paradigm. Web service discovery is one of the key issues in SOC. Web service discovery usually provide UDDI as a standard registry which features only keyword-based matches that often give poor performance. Currently approaches for semantic
In this paper, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of
Under grid environment, a resource discovery mechanism could dramatically affect both performance and efficiency of the system. Currently, however, since most models of grid discovery are centralized, keyword based matching ones, outcomes of discovery could seldom cater customers' needs. This paper puts forward a grid
machines interacting with other machines to yield results which are user oriented and precise. A New Integrated Case And Relation Based Page Rank Algorithm have been proposed to rank the results of a search system based on a user's topic or query. This paper proposes an optimized semantic searching of keywords represent by
analysis and results ranking. For semantic annotation, we use domain ontology and two bilingual dictionaries to extract keywords for annotation. For query analysis, we present a method which combines lexical relationship and semantic relationship to analyse user's query. And for results ranking, we propose a modulative method
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
Today's Environment, Web contributes greatly to the creation of an ever-increasing global information database. Web is a collection of billions of web pages. Web 2.0 is purely based on keyword based searching. Because of using keyword based search engine, people may get relevant or irrelevant information. The amount
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
library of XML documents which need to be ranked so they satisfy both the keyword and the tree structure constraints. This is a position paper which proposes combining existing vector space models (for keyword match) with tree isomorphism codes (for structural match).
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