Transcription factor (TF) is a general term for a variety of proteins or genes that regulate gene expression, which controls the activity of a gene by determining whether the gene’s DNA is transcribed into RNA. Alzheimer's Disease (AD) is a kind of neurodegenerative diseases which are associated with abnormal gene expression. However, the direct relations from literatures between TF and AD are very weak. This study aims to mining the latent relations between TF and AD by knowledge graph and semantic path analytics based on Literature-based discovery (LBD). Firstly, Subject-Predicate-Object (SPO) triples related are retrieved to construct a domain KG. Then, semantic paths with predications are extracted from KG using path traversal algorithm. After that we mine these semantic paths from four respects to reveal latent relations between TF and AD: key concepts identification, paths strength measurement and ranking, paths clustering, and emerging intermediate concepts prediction. Finally, the latent relations will be visualized and interpreted. The study is in process and more details could be presented on the conference. It will help inspire research ideas and make new discoveries to scientists.