With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre-training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use-cases from various domains.

中文翻译：

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知识图驱动的商业智能数据处理

随着大数据的激增，组织决策也变得更加复杂。商业智能 (BI) 不再仅限于查询营销和销售数据。它更多的是链接来自不同应用程序的数据，并翻阅大量非结构化数据（例如电子邮件、通话记录、社交媒体、新闻等），试图获得见解，从而为未来战略提供可操作的情报和更好的输入制作。知识图等语义技术已被证明是有用的工具，可以帮助智能地链接不同的数据源，并且还可以通过由于这种链接而创建的复杂网络进行推理。在过去的十年中，知识图的创建、存储和维护过程已经足够成熟，现在它们也正在进入业务决策领域。最近，这些图也被视为减少大型语言模型幻觉的潜在方法，通过在预训练和输出生成过程中包含这些图。也存在许多挑战。其中包括构建和维护图表、对缺失链接进行推理等等。虽然这些仍然是开放的研究问题，但我们在本文中介绍了目前如何使用知识图谱从各个领域的用例导出商业智能的调查。