数据科学与Vadminton：桥接机器学习和推理
Data Science with Vadalog: Bridging Machine Learning and Reasoning
摘要：
在最近大型科技公司的成功例子之后，许多现代企业寻求构建知识图谱，以提供企业知识的统一视图，并使用机器学习和逻辑推理来获得深刻的见解。目前，数据科学的传统方法（通常基于机器学习和统计建模）与使用领域知识进行推理的系统之间存在脱节。在本文中，我们提出了一个国家的最先进的知识图谱管理系统，Vadapter，它提供了高度的表达和高效的逻辑推理，并提供无缝集成与现代数据科学工具包，如数据库平台。我们演示了如何使用Vadmind来执行传统的数据处理任务，以及复杂的逻辑和概率推理。我们认为，这是在数据科学中将机器学习和推理结合起来的重要一步。
英文摘要：
Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
