An　Object　Summary　(OS)　is　a　tree　structure　of　tuples　that　summarizes　the　context　of　a　particular　Data　Subject　(DS)　tuple.　The　OS　has　been　used　as　a　model　of　keyword　search　in　relational　databases;　where　given　a　set　of　keywords,　the　objective　is　to　identify　the　DSs　tuples　relevant　to　the　keywords　and　their　corresponding　OSs.　However,　a　query　result　may　return　a　large　amount　of　OSs,　which　brings　in　the　issue　of　effectively　and　efficiently　ranking　them　in　order　to　present　only　the　most　important　ones　to　the　user.　In　this　paper,　we　propose　a　model　that　ranks　OSs　containing　a　set　of　identifying　keywords　(e.g.,　Chen)　according　to　their　relevance　to　a　set　of　thematic　keywords　(e.g.　Mining).　We　argue　that　the　effective　thematic　ranking　of　OSs　should　combine　gracefully　IR-style　properties,　authoritative　ranking　and　affinity.　Our　ranking　problem　is　modeled　and　solved　as　a　top-k　group-by　join;　we　propose　an　algorithm　that　computes　the　join　efficiently,　taking　advantage　of　appropriate　count　statistics　and　compare　it　with　baseline　approaches.　An　experimental　evaluation　on　the　DBLP　and　TPC-H　databases　verifies　the　effectiveness　and　efficiency　of　our　proposal.