We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.
翻译:我们对利用神经网络构建模型时将域知识纳入其中的方式进行了调查,纳入域知识不仅对建设科学助理具有特殊意义,而且对许多其他领域也具有特殊意义,这些领域涉及利用人体机械合作了解数据,在许多此类情况下,以机器为基础的模型建设可大大受益于以足够精确的形式对域进行编码的人类知识,本文件审查了将这种知识作为逻辑和数字限制进行编码的两种广泛方法,并描述了在每种方法下的若干子类中取得的技术和结果。