[目的/意义] 构建基于关联数据的探索式检索系统，充分利用关联数据中隐藏的知识网络，向用户提供发现新知识的机会。[方法/过程] 基于DBpedia电影数据集，采用改进的向量空间模型进行关联数据的语义相似度计算，使用可视化的交互技术对检索结果进行呈现。[结果/结论] 通过基于任务的评价方法与IMDB进行对比，证明基于关联数据的探索式检索系统能够提高检索效率，提升用户体验并能引导用户发现其感兴趣的信息。
[Purpose/significance] Building a LOD-baesd exploratory search system can make full use of the hidden knowledge behind the linked data, and then can provide users with new opportunities to discover new knowledge. [Method/process] Based on DBpedia movie dataset, we use improved Vector Space Model to calculate the semantic similarity and use visual interaction to show the search results. [Result/conclusion] By task-based evalution, comparing with IMDB, we prove that LOD-based exploratory search system can improve the efficiency of search and improve the user experience of search and also can guide the users to find the information they are interested in.
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