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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...
they do well for keyword search strings such as "ocean'08 conference information", they are quite inadequate for searching against structured data such as "time- series ocean surface temperature or salinity levels in the Gulf of Mexico". Traditional search engines deploy various complex algorithms, take into account the
The Internet is the largest information repository. Most information retrieval systems are based on the premise that users know the keywords for searching subjects. Web services provide a suitable technical framework for making business processes accessible within enterprises and across enterprises, so that they have
Most of the search engines search for keywords to answer the queries from users. The search engines usually search web pages for the required information. However they filter the pages from searching unnecessary pages by using advanced algorithms. These search engines can answer topic wise queries efficiently and
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
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.