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首页> 外文期刊>Journal of web semantics: >Explaining and predicting abnormal expenses at large scale using knowledge graph based reasoning
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Explaining and predicting abnormal expenses at large scale using knowledge graph based reasoning

机译：使用基于知识图的推理大规模解释和预测异常费用

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摘要

Global business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach. $1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimizing spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organizations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191,346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses. (c) 2017 Elsevier B.V. All rights reserved.
机译：2015年，全球商务旅行支出突破了创纪录的1.2万亿美元，并将达到这一水平。根据全球主要的商务旅行和会议贸易组织全球商务旅行协会（Global Business Travel Association）的数据，到2020年，美国的市值将达到1.6万亿美元。现有的支出系统旨在通过预定时间段（例如时间段，服务或员工组）报告支出，支出的类型和金额。但是，这种系统的目的不是系统地检测异常费用，更重要的是解释其原因。因此，从其分析中得出用于优化支出和节省的任何可行见解都是耗时，麻烦且通常是不可能的。为了应对这一挑战，我们提出了AIFS，这是一个为费用业务所有者和审计师设计的系统。我们的系统正在操纵和组合语义Web和机器学习技术，以用于（i）识别，（ii）解释和（iii）预测大型组织员工的异常费用索赔。我们的语义感知型员工费用分析和推理原型于2015年对191,346名独特的埃森哲员工进行了试验，展示了其在解释和预测异常费用方面的可扩展性和准确性。 （c）2017 Elsevier B.V.保留所有权利。

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