When　the　physical　space　and　the　cyber　space　are　linked　by　human,　Cyber-Physical　Society　(CPS)　has　emerged　and　produced　many　challenges.　Among　which,　the　challenge　of　fast　growing　data　and　knowledge　both　from　the　physical　space　and　the　cyber　space　has　become　a　crucial　issue.　Scalability　becomes　a　big　barrier　in　data　processing　(more　specifically,　search　and　reasoning).　Traditional　knowledge　processing　methods　aim　at　providing　users　complete　results　in　rational　time,　which　is　not　applicable　when　it　comes　to　very　large-scale　data.　While　in　the　context　of　Web　and　large-scale　data,　users＇　expectations　are　not　always　receiving　complete　results,　instead,　they　may　prefer　to　get　some　incomplete　subset　of　the　results　compared　to　waiting　for　a　long　time.　With　this　spirit,　an　approach　named　Interleaving　Reasoning　and　Selection　with　Knowledge　Summarization　(IRSKS)　is　developed.　This　approach　supports　incomplete　reasoning　and　heuristic　search　based　on　knowledge　summarization.　It　can　be　divided　into　two　phases:　the　off-line　and　the　on-line　processing.　The　off-line　processing　includes　partitioning　and　summarization,　and　provides　the　basis　for　heuristic　search.　Partitioning　makes　one　large-scale　dataset　become　many　small　subsets　(chunks).　Summarization　produces　summaries　that　contain　heuristic　information　(such　as　the　location　and　major　topics　of　each　chunk)　to　build　a　bridge　between　the　searching　target　and　the　partitioned　large-scale　dataset.　Through　the　cues　provided　by　summaries,　a　best　search　path　can　be　found　to　locate　the　searching　target.　Along　with　the　search　path,　the　on-line　processing　includes　interleaving　reasoning　and　selection,　which　compose　a　dynamic　searching　process　and　support　anytime　behavior.　In　this　way,　the　search　space　is　greatly　reduced　and　close　to　the　searching　target　so　that　a　good　trade-off　is　achieved　between　the　time　and　the　quality　of　a　query.　Based　on　this　approach,　a　prototype　system　named　Knowledge　Intensive　Summarization　System　(KISS)　has　been　developed　and　the　evaluation　with　the　KISS　system　on　the　PubMed　dataset　indicates　that　the　proposed　method　is　potentially　effective　for　processing　large-scale　semantic　data　in　the　Cyber-　Physical　Society.　©　2011　IEEE.