Artificial Intelligence (AI) is impacting our world. In the 1970s and 1980s, Expert Systems (ES) consisted of AI systems that included explicit knowledge, often represented in a symbolic form (e.g., by using the Prologue language), that was extracted from human experts. Since then, there has been an AI shift, due to three main phenomena (Darwiche, 2018): data explosion, with availability of several big data sources (e.g., social media, sensor data); computational power growth, following the famous Moore's law which states that computer processing capacity doubles every 2 years; and rise of sophisticated statistical and optimization techniques, including deep learning. Thus, rather than being expert‐driven, ES have become more data‐driven, with the focus on developing “computerized systems that use AI techniques to solve a specific real‐ world domain application task” (Cortez, Moro, Rita, King, & Hall, 2018).

Aiming to foster the interaction between two key ES areas, Knowledge Discovery (KD) and Business Intelligence (BI), a series of “Knowledge Discovery and Business Intelligence” (KDBI) tracks were held at the EPIA conference on Artificial Intelligence, with a total of six editions from 2009 to 2019. Since 2011, the track has a dedicated special issue published in Wiley's Expert Systems journal (EXSY) (Cortez & Santos, 2013, 2015, 2017, 2018). KD is the AI subfield that addresses the extraction of useful knowledge from raw data (Fayyad, Piatetsky‐Shapiro, & Smyth, 1996), while BI, also known as Business Analytics, is an umbrella term that includes methods and tools (e.g., data warehousing, dashboards and analytics) to obtain actionable knowledge from data (Ain, Vaia, DeLone, & Waheed, 2019).

This is the Fifth special issue on Knowledge Discovery and Business Intelligence and it includes extended versions of selected papers presented at the sixth KDBI thematic track of EPIA 2019, held in Vila Real, Portugal. The track received a total of 17 paper submissions, from which 10 papers were accepted to be presented at the EPIA 2019 conference. The special issue of the EYSY journal involved two rounds of reviews for the selected papers, performed by the program committee members of the sixth KDBI track of EPIA2019 and EXSY journal expert reviewers. After the two rounds, six papers were accepted for the EXSY special issue.

The accepted KDBI special issue papers reflect current AI methodological and application challenges. For instance, nowadays image, sound, sensor and social media data are becoming commonplace, thus there is a need to develop machine learning systems capable of processing such data and proving value in real‐world applications. Moreover, the data‐driven models should be understandable by the domain humans, in what is termed as Explainable AI (XAI). Finally, the extracted data‐driven knowledge should be actionable, allowing to better support managerial decision‐making. These challenges are addressed by the six papers published in this special issue, which are summarized in the next section.

人工智能（AI）正在影响我们的世界。在1970年代和1980年代，专家系统（ES）由AI系统组成，这些AI系统包括从人类专家那里提取的，通常以符号形式（例如，通过使用Prologue语言）表示的显式知识。此后，由于三个主要现象，人工智能发生了转变（Darwiche，2018）：数据爆炸，提供了多个大数据源（例如，社交媒体，传感器数据）；计算能力的增长遵循著名的摩尔定律，该定律指出计算机处理能力每两年增加一倍；以及复杂的统计和优化技术（包括深度学习）的兴起。因此，ES不再是由专家驱动，而是由数据驱动，而是专注于开发“使用AI技术解决特定的现实世界域应用任务的计算机系统”（Cortez，Moro，Rita，King和霍尔，2018年）。

为了促进知识发现（KD）和商业智能（BI）这两个关键的ES领域之间的互动，在EPIA人工智能会议上举行了一系列“知识发现和商业智能”（KDBI）研讨会，从2009年到2019年共发行了六个版本。自2011年以来，该曲目专门刊登在Wiley的专家系统期刊（EXSY）（Cortez＆Santos，2013年，2015年，2017年，2018年）中。KD是AI子领域，致力于从原始数据中提取有用的知识（Fayyad，Piatetsky‐Shapiro和Smyth，1996年），而BI也被称为Business Analytics，是一个总括的术语，包括从数据中获取可操作知识的方法和工具（例如，数据仓库，仪表板和分析）（Ain，Vaia，DeLone和Waheed，2019年）。

这是关于知识发现和商业智能的第五期特刊，其中包括在葡萄牙维拉雷亚尔举行的EPIA 2019的第六届KDBI主题轨道上展示的精选论文的扩展版本。该赛道共收到17篇论文，其中10篇论文被接受在EPIA 2019大会上发表。EYSY期刊的特刊涉及对所选论文的两轮审阅，由EPIA2019第六届KDBI曲目的程序委员会成员和EXSY期刊专家审阅者进行。在两轮之后，EXSY特刊接受了六篇论文。

公认的KDBI特殊问题论文反映了当前AI方法论和应用挑战。例如，如今图像，声音，传感器和社交媒体数据变得司空见惯，因此需要开发一种能够处理此类数据并在现实应用中证明其价值的机器学习系统。此外，数据驱动模型应该被领域人员理解，即所谓的可解释AI（XAI）。最后，提取的数据驱动知识应该是可行的，以便更好地支持管理决策。这些挑战通过本期特刊中发表的六篇论文得到了解决，这些论文将在下一部分中进行概述。