{ "paper_id": "O06-1020", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:07:08.579085Z" }, "title": "", "authors": [], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "O06-1020", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": ")", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\uf941 (Information Extraction", "sec_num": "1." }, { "text": "\uf996 \uf9be C4.5 \uf996 \uf965 \uf91d \uf9be \uf9be \uf9be \uf901 \uf9be \uf9e4 \uf905 \uf9be \uf9e4\uf969 \uf9be \uf91d \uf9b5 \uf967 \uf9be (data profiling) \uf9a3 \uf905 \uf905 \uf905 \uf91d \uf962 3. \uf9be \uf9b4 \uf9be \uf9be \uf9be\uf92d \uf9be\uf967 \uf9be \uf9be \uf9fc \uf9be \uf967 \uf9be \uf92d \uf97e \uf9fc \uf905 \uf9be \uf9f7 (\uf91d ) \uf905 \uf9be \uf9be \uf9be \uf9be \uf9fc (subject instance) \uf9be \uf91d \uf905 \uf9be \uf9fc \uf905 \uf91d \uf9be \uf94e \uf9fc \uf9ba \uf967 \uf9be \uf9be \uf962 1. (missing entities) \uf9fc \uf9be \uf9ba \uf9be 2. (missing values) \uf9fc \uf9fc \uf976\uf9ba \uf905 \uf9be 3. (duplicates) \uf9be \uf9be \uf9be 4. \uf967 (invalid values) \uf9fc \uf9fc \uf905 \uf91d \uf91d \uf967 \uf9f7 \uf9be \uf967 \uf967 \uf9e0 \uf92d \uf9be \uf962 \uf967 \uf967 \uf967 \uf91d \uf967 \uf9be \uf9be \uf9f7 \uf9be \uf967 \ufa09 \ufa08 3.1 \uf905 \uf905 \uf905 \uf9d0 \uf9be \uf905 \uf905\uf9d0 \uf9d0 \uf905 \uf905 \uf962 \uf92d \uf905 \uf905 \uf967 \uf9b4 \uf967 \uf905 \uf967 \uf905 \uf9a3 \uf9d1 \uf905 1. string cardinality ( S c ) \uf905 \uf969 2. string prefix ( S p ) \uf905 k k \uf96b\uf969 3. string suffix ( S s ) \uf905 k k \uf96b\uf969 4. string entity ( S e ) \uf905 \uf99c 5. string numeral ( S n ) \uf905 \uf969 true or false 6. string format ( S f ) \uf905 \uf9be SF \uf9d1 \uf905 } , , , , , { f n e s p c S S S S S S SF = SF \uf92d \uf9be (v i ) SF(v i ) = (S c (v i ), S p (v i ), S s (v i ), S e (v i ), S n (v i ), S f (v i )) \uf962 \uf91d \uf9b5 \uf963 \uf905 \uf905 \uf9b5 SF( \uf963 ) and k=1 S c S p S s S e S n S f 5 \uf963 false string 3.2 \uf905 \uf969 \uf967 \uf9f7 \uf962 \uf905 \uf96f \uf91d \uf9d0 \uf905 \uf905 \ufa0a \uf905 \ufa0a \uf905 \uf962 \uf967 \uf969 \uf91d \uf905 \uf969 \uf9be \uf9d1 \uf905 \uf969 \uf9e4 \uf9d1 \uf905 \uf91d \ufa26 \ufa0a \uf9d1 \uf969 \uf969 \uf969 \ufa26 \uf905 \uf91d \ufa26 \ufa0a \uf941 \uf962 } 6 , 5 , 4 , 3 , 2 , 1 { , \u2208 j S j , SF \uf905 \uf9be v i ) ( i j v S )) ( ( i j rob v S P \uf969 ) ( ' i j v S \uf969 \uf978 \uf905 \uf969 \ufa26 w w \uf96b\uf969 ) (w T \uf969 \uf99c \uf969 w \uf969 1 \uf967 w ) 10 ( T \uf965 0% 10% \uf969 1 10% 20% \uf969 2 \uf9d0 \uf969 10 \uf905 \ufa08 \uf969 ) ( ' i j v S ) ( )) ( ( ) ( ' w T v S P v S i j rob i j \u22c5 = \uf94f \uf905 \uf99c \uf94f \ufa08 G \uf967 )) ( ( i j rob v S P \uf969 ) ( ' i j v S \u2211 \u2208 \u2200 \u22c5 = G i i j rob i j w T v S P v S ) ( )) ( ( ) ( ' \uf969 \uf9b5\uf96f \uf9be \uf969 {1, 2, 3, 4, 5, 6} {1.5%, 11%, 79%, 5%, 3%, 0.5%} w \uf96b\uf969 10 \uf969 {1, 4, 5, 6} \ufa26 0% 10% \uf969 \ufa26 1 \uf969 {2} \ufa26 10% 20% \uf969 2 \uf969 {3} \ufa26 70% 80% \uf969 8 \uf94f \uf969 {6} \uf969 1 \uf969 {1} \uf94f \uf969 {6,1} \uf969 1 \uf92d \uf969 {5} \uf94f \uf969 {6,1,5} \uf969 1 \uf92d \uf969 {4} \uf94f \uf969 {6,1,5,4} \uf969 2 \uf92d \uf969 {2} \uf94f \uf969 {6,1,5,4,2} \uf969 3 \uf969 {3} \uf94f \uf969 {6,1,5,4,2,3} \uf969 10 \uf94f ) ( i c v S )) ( ( i c rob v S P ) ( ' i c v S ) ( i c v S \u2211 \u2208 \u2200 G i i c rob v S P )) ( ( ) ( ' i c v S 6 0.5 % 1 6 0.5 % 1 1 1.5 % 1 1 2 % 1 5 3 % 1 5 5 % 1 4 5 % 1 4 10 % 2 2 11 % 2 2 21 % 3 3 79 % 8 3 100 % 10 \ufa0a \uf905 \uf961 \uf969 \uf969 \uf962 \uf9ba \uf941 \uf969 \uf962 \uf969 \uf962 \uf974 \uf969 \uf969 \uf962 \uf969 \uf962 \uf967 \uf967 \uf9e0 \uf978 \ufa00 \uf967 \uf9d0 \uf967 \ufa08 3.3 \uf9be \uf9be \uf9d0 \ufa08 \uf978 \uf9d0 \uf9d0 \uf9ba \uf9f7 \uf9d0 \uf9d0 \uf996 \uf9be \uf905 \uf969 \uf9d0 \uf9d0 \uf9d0 \uf9be \ufa08 \uf9d0 \uf9fc \uf9be \uf9f7 \uf9be \uf9d0 (supervised machine learning) C4.5 \uf9e4\uf941(statistical learning theory) \uf97e (support vector machine) \uf9dd \uf905 \uf969 \uf9be \uf978 \uf967 \uf905 \uf9d0 \uf97e C4.5 \uf97e \ufa26 ( SVM \uf96b\uf969 \uf96b \uf9f7 LIBSVM [2]) \uf967 \uf905 \uf969 \uf9be C4.5 \uf97e \uf978 4. \uf9a8 \uf962 \uf9b5 \uf996 \uf9be \uf905 \uf969 \uf9be \uf9be \uf9d0 \uf967 [7] \uf9b4 \uf9fc \uf9e4 20 \uf98e 10 \uf962 \uf9be \uf962 \uf9be \uf9be( 1995 \uf98e 2004 \uf98e) \uf9be \uf996 \uf9be \uf9be \uf92d \uf9b5 4.1 \uf9f7 \uf9d0 \uf9be \uf962 \uf9be 2 x", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\uf941 (Information Extraction", "sec_num": "1." } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "An Extensible Framework for Data Cleaning", "authors": [ { "first": "H", "middle": [], "last": "Galhardas", "suffix": "" }, { "first": "D", "middle": [], "last": "Florescu", "suffix": "" }, { "first": "D", "middle": [], "last": "Shasha", "suffix": "" } ], "year": 1999, "venue": "INRIA Technical Report", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Galhardas, H., Florescu, D., and Shasha, D. 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