  The representation of knowledge based on first-order logic captures the
richness of natural language and supports multiple probabilistic inference
models. Although symbolic representation enables quantitative reasoning with
statistical probability, it is difficult to utilize with machine learning
models as they perform numerical operations. In contrast, knowledge embedding
(i.e., high-dimensional and continuous vectors) is a feasible approach to
complex reasoning that can not only retain the semantic information of
knowledge but also establish the quantifiable relationship among them. In this
paper, we propose recursive neural knowledge network (RNKN), which combines
medical knowledge based on first-order logic with recursive neural network for
multi-disease diagnosis. After RNKN is efficiently trained from manually
annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented
knowledge embeddings and weight matrixes are learned. Experimental results
verify that the diagnostic accuracy of RNKN is superior to that of some
classical machine learning models and Markov logic network (MLN). The results
also demonstrate that the more explicit the evidence extracted from CEMRs is,
the better is the performance achieved. RNKN gradually exhibits the
interpretation of knowledge embeddings as the number of training epochs
increases.
