The explosion in the size and the complexity of the available Knowledge Graphs on the web has led to the need for efficient and effective methods for their understanding and exploration. Semantic summaries have recently emerged as methods to quickly explore and understand the contents of various sources. However in most cases they are static not incorporating user needs and preferences and cannot scale. In this paper we present iSummary a novel scalable approach for constructing personalized summaries. As the size and the complexity of the Knowledge Graphs for constructing personalized summaries prohibit efficient summary construction, in our approach we exploit query logs. The main idea behind our approach is to exploit knowledge captured in existing user queries for identifying the most interesting resources and linking them constructing as such highquality personalized summaries. We present an algorithm with theoretical guarantees on the summarys quality linear in the number of queries available in the query log. We evaluate our approach using three realworld datasets and several baselines showing that our approach dominates other methods in terms of both quality and efficiency.

翻译：随着网络上可用知识图谱规模和复杂性的爆炸式增长，迫切需要高效且有效的方法来理解和探索这些图谱。语义摘要作为快速探索和理解不同数据源内容的方法应运而生。然而，在大多数情况下，这些摘要是静态的，未能融入用户需求和偏好，且缺乏可扩展性。本文提出了iSummary，一种新颖的可扩展方法，用于构建个性化摘要。由于构建个性化摘要所需的知识图谱规模庞大且结构复杂，严重阻碍了高效摘要的构建，因此我们的方法利用了查询日志。该方法的核心思想是通过挖掘现有用户查询中所蕴含的知识，识别最感兴趣的资源，并将它们链接起来，从而构建高质量的个性化摘要。我们提出了一种算法，该算法在摘要质量上具有理论保证，且其复杂度与查询日志中的查询数量呈线性关系。我们使用三个真实世界数据集和多个基线方法对方法进行了评估，结果表明，在质量和效率方面，我们的方法均优于其他方法。