In　this　paper,　we　look　at　the　problem　of　completing　an　open-world　knowledge　graph　by　describing　entities.　Many　open-world　knowledge　graph　completion　(KGC)　models　train　alignment　functions　that　map　embeddings　based　on　textual　descriptions　to　structural　embedding　spaces.　They　then　use　traditional　closed-world　KGC　models　to　predict　missing　facts.　When　generating　and　aligning　textual　embeddings　for　the　open　world　knowledge　graph　completion　task,　most　existing　approaches　ignore　the　effect　of　relations.　This　means　that　noise　from　the　texts　gets　into　feature　extraction,　which　hurts　model　performance.　So,　a　new　open-world　KGC　model　called　EmReCo　is　proposed.　It　is　based　on　relation-specific　constraints　and　focuses　on　relation-correlated　information　extraction　from　entity　descriptions　with　a　relation-aware　attention　aggregator　for　better　textual　embeddings.　A　relation-specific　gate　filtering　mechanism　is　also　made　to　keep　relation-specific　features　in　the　entity　embeddings.　Extensive　tests　on　two　benchmark　open-world　datasets　show　that　EmReCo　gets　great　results,　especially　with　long　text　datasets,　and　that　the　best　metric,　Hits@1,　can　get　better　by　8.1%.