Abstract
In order to solve the lack of knowledge relevance and topic drift in traditional keyword search, this paper proposed knowledge extraction strategy based knowledge point association and recognition of user intention based user intention theme map and the mapping from resource to knowledge points strategy .It realized the knowledge sestematic and relevant of the search results and improved the retrieval accuracy.
Highlights
In order to solve insufficient or unreasonable knowledge relevance and inaccurate recognition of user intention for traditional keyword search or semantic search, which results in problems of incoherent learning process,the lack of learning content and topic drift in basic education, this study improves the traditional semantic analysis methods and proposes the theme map of user intention as the carrier and query expansion of the knowledge point association, which provides the conditions for the semantic recognition of the retrieved content
The accuracy of query expansion is improved by recognizing the user intention, which ensures the retrieval accuracy and the degree of personalization
The semantic similarity calculated from the corpus and the structural similarity obtained from the knowledge base are multiplied by a certain proportion to obtain the final similarity, which can improve the accuracy of the calculation results compared with the results in[2]
Summary
In order to solve insufficient or unreasonable knowledge relevance and inaccurate recognition of user intention for traditional keyword search or semantic search, which results in problems of incoherent learning process ,the lack of learning content and topic drift in basic education, this study improves the traditional semantic analysis methods and proposes the theme map of user intention as the carrier and query expansion of the knowledge point association, which provides the conditions for the semantic recognition of the retrieved content
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