The　abundance　and　ubiquity　of　RDF　data　(such　as　DBpedia　and　YAGO2)　necessitate　their　effective　and　efficient　retrieval.　For　this　purpose,　keyword　search　paradigms　liberate　users　from　understanding　the　RDF　schema　and　the　SPARQL　query　language.　Popular　RDF　knowledge　bases　(e.g.,　YAGO2)　also　include　spatial　semantics　that　enable　location-based　search.　In　an　earlier　location-based　keyword　search　paradigm,　the　user　inputs　a　set　of　keywords,　a　query　location,　and　a　number　of　RDF　spatial　entities　to　be　retrieved.　The　output　entities　should　be　geographically　close　to　the　query　location　and　relevant　to　the　query　keywords.　However,　the　results　can　be　similar　to　each　other,　compromising　query　effectiveness.　In　view　of　this　limitation,　we　integrate　textual　and　spatial　diversification　into　RDF　spatial　keyword　search,　facilitating　the　retrieval　of　entities　with　diverse　characteristics　and　directions　with　respect　to　the　query　location.　Since　finding　the　optimal　set　of　query　results　is　NP-hard,　we　propose　two　approximate　algorithms　with　guaranteed　quality.　Extensive　empirical　studies　on　two　real　datasets　show　that　the　algorithms　only　add　insignificant　overhead　compared　to　non-diversified　search,　while　returning　results　of　high　quality　in　practice　(which　is　verified　by　a　user　evaluation　study　we　conducted).