Knowledge　representation　learning　attempts　to　represent　entities　and　relations　of　knowledge　graph　in　a　continuous　low-dimensional　semantic　space.　However,　most　of　the　existing　methods　such　as　TransE,　TransH,　and　TransR　usually　only　utilize　triples　of　knowledge　graph.　Other　important　information　such　as　relation　descriptions　with　relevant　knowledge　is　still　used　ineffectively.　To　address　these　issues,　in　this　paper,　we　propose　a　relation　text-embodied　knowledge　representation　learning　method,　in　which　relation　descriptions　are　adopted　as　side　information　for　representation　learning.　More　specifically,　we　explore　a　convolutional　neural　model　to　build　representations　of　fine-grained　relation　descriptions.　Furthermore,　knowledge　representations　of　triples　and　representations　of　fine-grained　relation　descriptions　are　jointly　embedding.　Our　model　is　evaluated　on　the　tasks　of　both　link　prediction　and　triple　classification.　The　experiment　results　show　that　our　model　exhibits　a　superior　performance　than　other　baselines,　which　demonstrates　the　availability　of　our　method　with　fine-grained　relation　descriptions　and　knowledge　graph　jointly　embedding.