Group　activity　recognition　is　a　challenging　task　because　it　involves　diverse　individual　actions　and　complex　relations.　Most　existing　methods　enhance　individual　representation　by　introducing　relation　inference　using　appearance　features.　Some　methods　utilize　extra　knowledge,　such　as　action　labels,　to　enhance　relation　inference　and　refine　the　individual　representation,　but　the　knowledge　they　explored　is　simple　and　insufficient.　In　this　paper,　we　propose　a　novel　idea　of　knowledge　concretization　and　further　develop　a　Knowledge　Augmented　Relation　Inference　framework　(KARI)　for　group　activity　recognition.　Specifically,　we　first　concretize　knowledge　from　training　data,　and　then　represent　them　as　Class-Class　co-occurrence　Map　(C-C　Map)　and　Class-Position　distribution　Map　(C-P　Map).　On　top　of　them,　KARI　explores　concretized　knowledge　to　integrate　visual　and　semantic　representation　in　a　unified　architecture　for　group　activity　recognition.　Experimental　results　on　two　public　datasets　show　that　the　proposed　framework　performs　favorably　compared　with　state-of-the-art　approaches.　IEEE