The　goal　of　biomedical　relation　extraction　is　to　obtain　structured　information　from　electronic　medical　records　by　identifying　relations　among　clinical　entities.　By　integrating　the　advantages　of　unsupervised　and　semi-supervised　learning,　the　distant　supervision　approach　has　achieved　significant　success　for　a　relation　extraction　task　without　a　large　amount　of　labeled　corpora.　However,　in　many　cases,　the　recognized　entities　from　the　Chinese　clinical　text　are　not　defined　in　semantic　knowledge　base,　which　limits　the　application　of　distant　supervision　for　biomedical　relation　extraction.　This　work　proposes　a　Knowledge　Guided　Distance　Supervision　(KGDS)　model　for　handling　the　biomedical　relation　extraction　task　in　Chinese　electronic　medical　records.　To　handle　the　unknown　entities,　entity-type　alignment　(instead　of　entity　alignment　in　traditional　distant　supervision)　is　employed　for　extracting　coarse-grained　relations.　Then,　by　learning　the　relation　embeddings　both　from　semantic　knowledge　base　and　electronic　medical　record　dataset　as　knowledge-enhanced　features,　this　work　presents　a　knowledge-enhanced　bootstrapping　learning　process　for　fine-grained　relation　disambiguation.　The　empirical　experiments　on　the　real-world　dataset　of　electronic　medical　records　illustrate　that　our　KGDS　model　achieves　the　best　performance　comparing　to　other　state-of-the-art　models,　thereby　advancing　the　field　of　biomedical　relation　extraction　from　Chinese　electronic　medical　records.