Time-Aware Probabilistic Knowledge Graphs
Time-Aware Probabilistic Knowledge Graphs
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DOI：
10.4230/lipics.time.2019.8
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Chekol;H. Stuckenschmidt
中科院分区：
文献类型：
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作者：
M. Chekol;H. Stuckenschmidt
作为构建和扩展知识图谱的工具，开放信息抽取的出现促进了时间数据的增长，例如YAGO、NELL和Wikidata。YAGO和Wikidata维护事实的有效时间，而NELL记录从某些Web语料库检索事实的时间点。总的来说，这些知识图（KG）存储了从Wikipedia和其他来源提取的事实。由于用于构建和扩展KG（例如NELL）的提取工具的不精确性，KG中的事实是加权的（表示事实正确性的置信度值）。此外，NELL可以被视为事务时间KG，因为每个事实都与提取日期相关联。另一方面，YAGO和Wikidata使用有效时间模型，因为它们将事实与有效时间（时间范围）一起保存。本文提出了一种双时概率知识图的维护和查询双时模型（结合了事务模型和有效时间模型）。研究了边际推理和MAP推理的聚并性和可扩展性。此外，我们表明，在非时概率KG推理任务的复杂性延续到双时设置。最后，我们报告了我们对所提模型的评估结果。2012 ACM主题分类信息系统→Web本体语言（OWL）；计算方法→概率推理；计算方法→时间推理
The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model. 2012 ACM Subject Classification Information systems → Web Ontology Language (OWL); Computing methodologies → Probabilistic reasoning; Computing methodologies → Temporal reasoning