The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Internets are important in everyone's life like searching keyword, college, social network and online shopping, when user using the internet for searching the keyword they getting some problem. That is when user searching for the keyword for some meaning but they will get different meaning for that keyword. Because
) 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
In order to solve the problem brought from the enormous policy texts and the complex management in the social insurance field, the article uses ontology as the way of representing and storing the knowledge. The article first constructed the framework of the ontology through manual work to ensure the relative accuracy of the ontology structure. Then it achieved the automatic ontology expansion based...
called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic
Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based
information in a authentication way. Ontology ranked keyword search algorithm utilized to analyze and filter search queries and rank results accordingly. Users search history is stored only locally and search results are provided by the server in partiality to existing search engine history information. The search history
keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to
Social annotation provides a convenient way to annotate shared content by allowing users to use any tag or keyword. While free folksonomy is widely used in social software implementations and especially in web services, it will play an important role in the semantic web services. However, such tags cannot offer the
experiment was made to test the effect of the system. The satisfying results were obtained which proved that the system could effectively improve the performance of the agricultural domain FAQ retrieval system comparing with the baseline keyword FAQ system.
Web Service classification becomes more essential with the increasing number of Web Services. The current practical approach for management and discovery is based on keyword search techniques. In this paper, we present an approach based on semantic reasoning and ontology techniques in order to organize web services
Domain Assets are the domain knowledge constructed according to the common requirements in the domain. In order to reuse the domain assets effectively, a domain assets search algorithm is proposed in this paper. Compared with the keyword search, this algorithm is based on semantic similarity, and the domain assets
the definition questions, the sentences or paragraphs with higher relevance can be extracted to become the answer based on the relevance ranking between the candidate sentences or paragraphs and questions by combined the computing method of keywords weighting and the method of semantic similarity between the sentences
their scalability and efficiency. The problem of extracting knowledge from huge amount of data is recorded as an issue in the medical sector. In this paper, we aim to improve knowledge representation by using MeSH Ontology on medical theses data by analyzing the similarity between the keywords within the theses data and
This work aims to find the medical information suited to the conditions, by analyzing the keywords provided from an user. With the given information, this model can be used to provide a flexible, compatible and effective searching paper for medical information. The proposed framework which helps medical institutions
fuzzy Euclidean distance clustering algorithm after using MeSH ontology on medical theses data for better categorization of the keywords within the data.
to extract the principle moves of the instructor strategies and related keywords. A special virtual classroom was developed for simultaneous capturing of teacher dialogue moves and additional VCR tools, along with student responses to construct the model information.
standardize research keywords. Secondly, frequent item sets with different support degrees are extracted from research proposals based on research ontology. Thirdly, a new measure of similarity degree between two research proposals is developed and then a clustering algorithm is proposed to classify research proposals based on
. In this approach, first an ontology is constructed to standardize research keywords, and then a frequent item set with various degrees of support is extracted from the research proposals, based on the ontology. In their paper on Success of Multi Criteria Decision Support Systems: The Relevance of Trust, Maida, Maier
review. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.