Scaling Probabilistic Temporal Query Evaluation
Scaling Probabilistic Temporal Query Evaluation
复制标题
DOI：
10.1145/3132847.3133038
复制
发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
M. Chekol
中科院分区：
文献类型：
--
作者：
M. Chekol
开放信息提取驱动了（时态）知识图（例如YAGO）的自动构建，这些知识图维护了概率（时态）事实和推理规则。这些知识图中最重要的任务之一是查询评估。这个任务是众所周知的#P-hard。概率（时间）查询评估的瓶颈之一是找到有效的方法来建立查询和推理规则，以生成可用于近似查询评估或检索查询谱系以进行精确评估的因子图。在这项工作中，我们提出了PRATiQUE （PRobAbilistic Temporal QUery Evaluation）框架，用于可扩展的时间查询评估。它利用时间推理规则的结构来有效地在数据库内建立基础，即，它使用分区来存储结构等效的规则。此外，PRATiQUE利用最先进的吉布斯采样器来计算查询答案的边际概率。我们报告了一项广泛的实验评估，证实了我们建议的有效性。
Open information extraction has driven automatic construction of (temporal) knowledge graphs (e.g. YAGO) that maintain probabilistic (temporal) facts and inference rules. One of the most important tasks in these knowledge graphs is query evaluation. This task is well known to be #P-hard. One of the bottlenecks of probabilistic (temporal) query evaluation is finding efficient ways of grounding the query and inference rules, to generate a factor graph that can be used for approximate query evaluation or to retrieve lineages of queries for exact evaluation. In this work, we propose the PRATiQUE (PRobAbilistic Temporal QUery Evaluation) framework for scalable temporal query evaluation. It harnesses the structure of temporal inference rules for efficient in-database grounding, i.e., it uses partitions to store structurally equivalent rules. Besides,PRATiQUE leverages a state-of-the-art Gibbs sampler to compute marginal probabilities of query answers. We report on an extensive experimental evaluation, which confirms the efficiency of our proposal.