Towards Probabilistic Bitemporal Knowledge Graphs
Towards Probabilistic Bitemporal Knowledge Graphs
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DOI：
10.1145/3184558.3191637
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发表时间：
2018-04
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
开放式信息提取作为构建和扩展知识图的工具的出现有助于时态数据的增长，例如YAGO，NELL和Wikidata。虽然YAGO和Wikidata维护事实的有效时间，但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）存储从维基百科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中的事实被加权（代表事实正确性的置信度值）。此外，NELL可以被认为是事务时间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，因为它们只维护事实及其有效时间（时间范围）。在本文中，我们提出了一个双时态模型（结合事务和有效时间模型）的维护和查询概率时态知识图。我们报告我们的评估结果所提出的方法。
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 (KGs) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KGs, such as NELL, the facts in the KGs 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 only 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 probabilistic temporal knowledge graphs. We report our evaluation results of the proposed approach.