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Inspired by the great success of information retrieval (IR) stylekeyword search on the Web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: (1) Identify the user search intention, i.e. identify the XML node types that user wants to
the traditional method to retrieve content based on the contents of the title, author, and time to search. For instance, concerning a large amount of construction work on resources preparing, low query efficiency, and being unable to search out interrelated content and so on. In this paper, a relational model of semantic
important in IoT for the Information Systems Research community as well as the first overview of the keywords that the authors use to describe their work in IoT- related context. Publications from the IoT context, including some of the topic areas in smart environment from the AIS electronic library were analyzed towards their
models are “right enough” to aid in decision making for important practical problems. Over the past fifty years, simulation has transformed from its beginnings as a brute-force numerical integration method into an attractive and sophisticated option for decision makers. This is due, in part, to the exponential
as titles, abstracts, keywords and the Chinese Library Classification Codes (CLCCs). According to the reviewer's interest model, we then propose a recommendation approach, which can send a paper published online to the reviewers that are experts in the scoop of the paper. Experimental results show that our
Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text
Search results in technical forums are typically keyword based. The relevance of a link is usually gauged by closest content match. However, it has been shown in literature that users' click behavior is an integral part of deciding the relevance of a search result. Moreover, it is not just the number of clicks that
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