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The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems (e.g. professional search systems, web search engines) with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for providing an overview...
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
queries, reverse queries, Webpage title and keyword phrases are combined with the cluster centers to attain high-quality expansion terms for new queries. We also propose a new terminology extraction method through Baidu Baike. It can identify and extract the terminology phrase based on the manual edited dictionary online.
The Web has the potential to become the world's largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword
In the age of Internet, with the online information explosive growth, people want to find information we need in the cyberworld fleetly and exactly. The information retrieval method based on the keyword or the simple logic-combination of the keywords has been unable to meet the people's need of information getting to
their query capabilities, we build and query semantic layers for three different types of web archives. An experimental evaluation showed that a semantic layer can answer information needs that existing keyword-based systems are not able to sufficiently satisfy.
do not have enough links between resources, and the LOD need a lot of time for creation. Therefore, this paper presents the new LOD conversion system that can convert the Web contents to the LOD. This system extracts keywords from sentences in the Web contents using DBpedia LOD, and generates the knowledge base. By
inherited the probability from multiple fathers. We used N-gram based on Wikipedia words to extract the keywords from web page, and introduce Bayes classifier to estimate the page class probability. Experimental results shown that the proposed method has very good scalability, robustness and reliability for different web pages.
Web page recommendation model traces userspsila Web-surfing trails, extracts the useful information including keywords, Web page URLs and userspsila evaluations on Web pages, and automatically generates FCA (formal concept analysis) knowledge base and enterprise ontology knowledge base with WordNet. While users are
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