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Artificial Intelligence and web when amalgamated, it may produce miracle in terms of semantic web and its one of the important application is in E-Learning. E-learning works efficiently only when E-Content preparation is need based, customizable and on-demand, preparation is real-time, searchable through keyword/s
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
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
) 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
Knowledge management and retrieval are important parts of an e-learning system. The retrieval intention is hard to express by traditional keywords based retrieval mode. In this paper, based on analyzing keywords in semantic layer, semantic similarity is calculated to catch the user's retrieval intention. Finally, the
Various researchers have recognized that ontology based retrieval is one of the best in terms of precision and recall for semantic search engine. However, they did not specify explicitly any scheme/ algorithm for ontology based indexing and retrieval. This motivates us to conceive a proposal that includes (a) suggesting indexing algorithm to effectively maintain ontologies, (b) suggesting retrieving...
Current keyword based search engines are not able to search semantics in web pages. In this paper we present framework for semantic based web content mining system using semantic ontology and SPARQL. A major challenge in the work involves building ontology database from natural language web pages. Second challenge is
a better precision (28% improvement) and recall (25% improvement) when compared to keyword based search, when used in a big set of representative biodiversity data (206,000 records) from INPA and the Emilio Gueldi Museum in Pará (MPEG). We also show that, because the biodiversity data is now in semantic web
Nowadays people love travel to escape the grind of regular daily life, for the sake of convenience to web information sourced on internet with most search engines, the journey tours are easily found, but the feedbacks are compared with the keyword you entered, not truly responded to your intention, therefore, diverse
This paper focuses on work done in Conference Domain. The overall objective of our project is to create and maintain the information in Conference domain by introducing semantic technologies. This paper analyzes the limitations in keyword based search techniques and the need for semantic based intelligent information
currently, the World Wide Web is a medium for storing and sharing data. Search based on keyword matching and content classified are two methods of the traditional search, however, their performance of processing exponential growth data is unsatisfactory. The concept of the Semantic Web, which was proposed to solve the
The existing expert information systems supply services for searching information of experts and help the users to select suitable experts to evaluate all types of projects in different fields, however, almost all of these search services use keyword match, and it is hard to gain high search efficiency. Ontology has
In this paper, we have developed a framework for ontology generalization that can easily generalize two or more similar type ontologies into one ontology. The proposed system first checks the similarity among the ontologies. It then merges the similar ontologies into one more general ontology. We have tested our developed ontology generalization approach with several ontologies. We have found that...
Artificial Intelligence and web when amalgamated, it may produce miracle in terms of semantic web. E-learning works efficiently only when E-Content preparation is need based, searchable through semantic similarity between keywords. This paper describes a e-content/e-book preparation from existing available contents on
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
Nowadays growing number of popularization in the World Wide Web promotes e-learning via web. During e-learning the users can easily share, reuse, and organize the knowledge. Using the search engine the e-learners search the web pages by set of keywords. But the pages which are unrelated for our tags come frequently
With the exponential growth in web content and due to its sheer volume, the answers provided by traditional search engines by query specific keywords to content has resulted in markedly high recall and low precision. In order to alleviate this problem, the notion of incorporating semantics in content and in Search
With the increasing popularity of Web 2.0, a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization
In order to overcome the defects of traditional-method filtering which based on keywords, the OWL text filtering is presented in the semantic Web environment, which makes information filtering, has been raised to the level of semantics. Through distinguishing the information between the title and text, and then
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