In conceptual modeling (CM), humans apply abstraction to represent excerpts of reality for means of understanding and communication, and processing by machines. Artificial Intelligence (AI) is applied to vast amounts of data to automatically identify patterns or classify entities. While CM produces comprehensible and explicit knowledge representations, the outcome of AI algorithms often lacks these qualities while being able to extract knowledge from large and unstructured representations. Recently, a trend toward intertwining CM and AI emerged. This systematic mapping study shows how this interdisciplinary research field is structured, which mutual benefits are gained by the intertwining, and future research directions.

翻译：在概念建模（CM）中，人类运用抽象方法对现实片段进行表征，以促进理解、沟通及机器处理。人工智能（AI）则通过海量数据自动识别模式或分类实体。概念建模能产出可理解且显式的知识表征，而人工智能算法的成果虽可从大规模非结构化数据中提取知识，却往往缺乏此类特性。近年来，概念建模与人工智能交叉融合的研究趋势日益显著。本项系统性映射研究揭示了这一跨学科研究领域的结构脉络、交叉融合所创造的相互增益价值，以及未来研究方向。