Approximating the Schema of a Set of Documents by Means of Resemblance
The WWW contains a huge amount of documents. Some of them share the same subject, but are generated by different people or even by different organizations. A semi-structured model allows to share documents that do not have exactly the same structure. However, it does not facilitate the understanding...
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| Published in | Journal on data semantics Vol. 7; no. 2; pp. 87 - 105 |
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| Main Authors | , , |
| Format | Journal Article Publication |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2018
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1861-2032 1861-2040 |
| DOI | 10.1007/s13740-018-0088-0 |
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| Abstract | The WWW contains a huge amount of documents. Some of them share the same subject, but are generated by different people or even by different organizations. A semi-structured model allows to share documents that do not have exactly the same structure. However, it does not facilitate the understanding of such heterogeneous documents. In this paper, we offer a characterization and algorithm to obtain a representative (in terms of a resemblance function) of a set of heterogeneous semi-structured documents. We approximate the representative so that the resemblance function is maximized. Then, the algorithm is generalized to deal with repetitions and different classes of documents. Although an exact representative could always be found using an unlimited number of optional elements, it would cause an overfitting problem. The size of an exact representative for a set of heterogeneous documents may even make it useless. Our experiments show that, for users, it is easier and faster to deal with smaller representatives, even compensating the loss in the approximation. |
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| AbstractList | The WWW contains a huge amount of documents. Some of them share the same subject, but are generated by different people or even by different organizations. A semi-structured model allows to share documents that do not have exactly the same structure. However, it does not facilitate the understanding of such heterogeneous documents. In this paper, we offer a characterization and algorithm to obtain a representative (in terms of a resemblance function) of a set of heterogeneous semi-structured documents. We approximate the representative so that the resemblance function is maximized. Then, the algorithm is generalized to deal with repetitions and different classes of documents. Although an exact representative could always be found using an unlimited number of optional elements, it would cause an overfitting problem. The size of an exact representative for a set of heterogeneous documents may even make it useless. Our experiments show that, for users, it is easier and faster to deal with smaller representatives, even compensating the loss in the approximation. The WWW contains a huge amount of documents. Some of them share the same subject, but are generated by different people or even by different organizations. A semi-structured model allows to share documents that do not have exactly the same structure. However, it does not facilitate the understanding of such heterogeneous documents. In this paper, we offer a characterization and algorithm to obtain a representative (in terms of a resemblance function) of a set of heterogeneous semi-structured documents. We approximate the representative so that the resemblance function is maximized. Then, the algorithm is generalized to deal with repetitions and different classes of documents. Although an exact representative could always be found using an unlimited number of optional elements, it would cause an overfitting problem. The size of an exact representative for a set of heterogeneous documents may even make it useless. Our experiments show that, for users, it is easier and faster to deal with smaller representatives, even compensating the loss in the approximation. Peer Reviewed |
| Author | Abelló, Alberto de Palol, Xavier Hacid, Mohand-Saïd |
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| References | ZhangZShashaDSimple fast algorithms for the editing distance between trees and related problemsSIAM J Comput198918612451262102547210.1137/02180820692.68047 Moh D-H, Lim E-P, Ng W-K (2000) Re-engineering structures from Web documents. In: 5th ACM conference on digital libraries (DL 2000). ACM, pp 67–76 Sanz I, Pérez J, Berlanga R, Aramburu M (2003) XML schemata inference and evolution. In: Proceedings of 14th international conference on databases and expert systems applications (DEXA’03), LNCS, vol 2736. Springer, pp 109–118 Jung J-S, Oh D-I, Kong Y-H, Ahn J-K (2002) Extracting information from XML documents by reverse generating a DTD. In: Proceedings of the EurAsia-ICT 2002, LNCS, vol 2510. Springer, pp 314–321 BertinoEGuerriniGMesitiMA matching algorithm for measuring the structural similarity between an XML document and a DTD and its applicationsInf Syst2004291234610.1016/S0306-4379(03)00031-0 W3C, Extensible Markup Language (XML) 1.0, 3rd Edition (February 2004) BexGJGeladeWNevenFVansummerenSLearning deterministic regular expressions for the inference of schemas from XML dataACM Trans Web20104414:114:3210.1145/1841909.1841911 Wang K, Liu H (1997) Schema discovery for semistructured data. In: 3rd International conference on knowledge discovery and data mining (KDD-97), pp 271–274 WidomJData management for XML: research directionsIEEE Data Eng Bull19992234452 Boobna U, de Rougemont M (2004) Correctors for XML data. In: Proceedings of 2nd international XML database symposium (XSYM’04), LNCS, vol 3186. Springer, pp 97–111 AlbertJGiammarresiDWoodDNormal form algorithms for extended context-free grammarsTheor Comput Sci20012671–23547185565310.1016/S0304-3975(00)00294-20984.68092 MinJ-KAhnJ-YCungC-WEfficient extraction of schemas for XML documentsInform Process Lett200385712195015610.1016/S0020-0190(02)00345-91042.68040 NayakRIryadiWXML schema clustering with semantic and hierarchical similarity measuresKnowl Based Syst200720433634910.1016/j.knosys.2006.08.006 BaaderFCalvaneseDMcGuinnessDNardiDPatel-SchneiderPThe description logic handbook2003CambridgeCambridge University Press1274.68451 Teege G (1994) Making the difference: a substraction operation for description logics. In: Proceedings of the international conference on principles of knowledge representation and reasoning (KR’94). Morgan Kaufmann, pp 540–550 AbiteboulSBunemanPSuciuDData on the Web–from relations to semistructured data and XML2000BurlingtonMorgan Kaufmann DalamagasTChengTWinkelK-JSellisTA methodology for clustering XML documents by structureInform Syst20063118722810.1016/j.is.2004.11.0091128.68345 WangLHassanzadehOZhangSShiJJiaoLZouJWangCSchema management for document storesProc VLDB Endow20158992293310.14778/2777598.2777601 GarofalakisMGionisARastogiRSechadriSShimKXTRACT: learning document type descriptors from XML document collectionsData Min Knowl Discov2003712356197370510.1023/A:1021560618289 BatageljVBrenMComparing resemblance measuresJ Classif19951217390134945310.1007/BF012022680833.62054 Nestorov S, Abiteboul S, Motwani R (1998) Extracting schema from semistructured data. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD 1998). ACM, pp 295–306 Hegewald J, Naumann F, Weis M (2006) XStruct: efficient schema extraction from multiple and large XML documents. In: Proceedings of the 22nd international conference on data engineering workshops, ICDE 2006, 3–7 Apr 2006, Atlanta, p 81 Izquierdo JLC, Cabot J (July 8-12, 2013) Discovering implicit schemas in JSON data. In: Web engineering—13th international conference, ICWE 2013, Aalborg, Proceedings, 2013, pp 68–83 Moh D-H, Lim E-P, Ng W-K (2000) DTD-miner: a tool for mining DTD from XML documents. In: Second international workshop on advance issues of E-commerce and web-based information systems (WECWIS 2000). IEEE Computer Society, pp 144–151 Estivill-Castro V, Yang J (2000) Fast and robust general purpose clustering algorithms. In: Proceedings of 6th Pacific Rim international conference on artificial intelligence (PRICAI 2000), LNCS, vol 1886. Springer, pp 208–218 GuerriniGMesitiMSanzIAkaliAPallisGAn overview of similarity measures for clustering XML documentsEmerging techniques and technologies: web data management practices2007HersheyIGI Global567810.4018/978-1-59904-228-2.ch003 LeonovAVKhusnutdinovRRStudy and development of the DTD generation system for XML documentsProgram Comput Softw200531419721010.1007/s11086-005-0032-61103.68479 LianWCheungDMamoulisNYiuS-MAn efficient and scalable algorithm for clustering XML documents by structureIEEE Trans Knowl Data Eng2004161829610.1109/TKDE.2004.1264824 Abiteboul S (1997) Querying semi-structured data. In: Proceedings of 6th international conference on database theory (ICDT’97), LNCS, vol 1186. Springer, pp 1–18 GallinucciEGolfarelliMRizziSSchema profiling of document-oriented databasesInform Syst201875132510.1016/j.is.2018.02.007 Klettke M, Störl U, Scherzinger S (2015) Schema extraction and structural outlier detection for JSON-based NOSQL data stores. In: Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 4.-6.3.2015 in Hamburg, Proceedings, pp 425–444 E Bertino (88_CR2) 2004; 29 88_CR22 W Lian (88_CR27) 2004; 16 88_CR21 Z Zhang (88_CR24) 1989; 18 E Gallinucci (88_CR28) 2018; 75 GJ Bex (88_CR16) 2010; 4 J Widom (88_CR11) 1999; 22 R Nayak (88_CR10) 2007; 20 88_CR25 M Garofalakis (88_CR7) 2003; 7 J Albert (88_CR6) 2001; 267 T Dalamagas (88_CR23) 2006; 31 J-K Min (88_CR20) 2003; 85 S Abiteboul (88_CR3) 2000 V Batagelj (88_CR26) 1995; 12 88_CR31 88_CR30 88_CR17 88_CR1 88_CR18 L Wang (88_CR4) 2015; 8 88_CR15 88_CR13 88_CR5 88_CR14 88_CR8 G Guerrini (88_CR12) 2007 AV Leonov (88_CR19) 2005; 31 88_CR9 (88_CR29) 2003 |
| References_xml | – reference: WidomJData management for XML: research directionsIEEE Data Eng Bull19992234452 – reference: NayakRIryadiWXML schema clustering with semantic and hierarchical similarity measuresKnowl Based Syst200720433634910.1016/j.knosys.2006.08.006 – reference: GarofalakisMGionisARastogiRSechadriSShimKXTRACT: learning document type descriptors from XML document collectionsData Min Knowl Discov2003712356197370510.1023/A:1021560618289 – reference: BertinoEGuerriniGMesitiMA matching algorithm for measuring the structural similarity between an XML document and a DTD and its applicationsInf Syst2004291234610.1016/S0306-4379(03)00031-0 – reference: Nestorov S, Abiteboul S, Motwani R (1998) Extracting schema from semistructured data. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD 1998). ACM, pp 295–306 – reference: Izquierdo JLC, Cabot J (July 8-12, 2013) Discovering implicit schemas in JSON data. In: Web engineering—13th international conference, ICWE 2013, Aalborg, Proceedings, 2013, pp 68–83 – reference: Sanz I, Pérez J, Berlanga R, Aramburu M (2003) XML schemata inference and evolution. In: Proceedings of 14th international conference on databases and expert systems applications (DEXA’03), LNCS, vol 2736. Springer, pp 109–118 – reference: ZhangZShashaDSimple fast algorithms for the editing distance between trees and related problemsSIAM J Comput198918612451262102547210.1137/02180820692.68047 – reference: Moh D-H, Lim E-P, Ng W-K (2000) DTD-miner: a tool for mining DTD from XML documents. In: Second international workshop on advance issues of E-commerce and web-based information systems (WECWIS 2000). IEEE Computer Society, pp 144–151 – reference: BexGJGeladeWNevenFVansummerenSLearning deterministic regular expressions for the inference of schemas from XML dataACM Trans Web20104414:114:3210.1145/1841909.1841911 – reference: Boobna U, de Rougemont M (2004) Correctors for XML data. In: Proceedings of 2nd international XML database symposium (XSYM’04), LNCS, vol 3186. Springer, pp 97–111 – reference: Abiteboul S (1997) Querying semi-structured data. In: Proceedings of 6th international conference on database theory (ICDT’97), LNCS, vol 1186. Springer, pp 1–18 – reference: AbiteboulSBunemanPSuciuDData on the Web–from relations to semistructured data and XML2000BurlingtonMorgan Kaufmann – reference: GuerriniGMesitiMSanzIAkaliAPallisGAn overview of similarity measures for clustering XML documentsEmerging techniques and technologies: web data management practices2007HersheyIGI Global567810.4018/978-1-59904-228-2.ch003 – reference: LianWCheungDMamoulisNYiuS-MAn efficient and scalable algorithm for clustering XML documents by structureIEEE Trans Knowl Data Eng2004161829610.1109/TKDE.2004.1264824 – reference: W3C, Extensible Markup Language (XML) 1.0, 3rd Edition (February 2004) – reference: Teege G (1994) Making the difference: a substraction operation for description logics. In: Proceedings of the international conference on principles of knowledge representation and reasoning (KR’94). Morgan Kaufmann, pp 540–550 – reference: Hegewald J, Naumann F, Weis M (2006) XStruct: efficient schema extraction from multiple and large XML documents. In: Proceedings of the 22nd international conference on data engineering workshops, ICDE 2006, 3–7 Apr 2006, Atlanta, p 81 – reference: Jung J-S, Oh D-I, Kong Y-H, Ahn J-K (2002) Extracting information from XML documents by reverse generating a DTD. In: Proceedings of the EurAsia-ICT 2002, LNCS, vol 2510. Springer, pp 314–321 – reference: MinJ-KAhnJ-YCungC-WEfficient extraction of schemas for XML documentsInform Process Lett200385712195015610.1016/S0020-0190(02)00345-91042.68040 – reference: AlbertJGiammarresiDWoodDNormal form algorithms for extended context-free grammarsTheor Comput Sci20012671–23547185565310.1016/S0304-3975(00)00294-20984.68092 – reference: Wang K, Liu H (1997) Schema discovery for semistructured data. In: 3rd International conference on knowledge discovery and data mining (KDD-97), pp 271–274 – reference: Klettke M, Störl U, Scherzinger S (2015) Schema extraction and structural outlier detection for JSON-based NOSQL data stores. In: Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 4.-6.3.2015 in Hamburg, Proceedings, pp 425–444 – reference: BatageljVBrenMComparing resemblance measuresJ Classif19951217390134945310.1007/BF012022680833.62054 – reference: LeonovAVKhusnutdinovRRStudy and development of the DTD generation system for XML documentsProgram Comput Softw200531419721010.1007/s11086-005-0032-61103.68479 – reference: GallinucciEGolfarelliMRizziSSchema profiling of document-oriented databasesInform Syst201875132510.1016/j.is.2018.02.007 – reference: Estivill-Castro V, Yang J (2000) Fast and robust general purpose clustering algorithms. In: Proceedings of 6th Pacific Rim international conference on artificial intelligence (PRICAI 2000), LNCS, vol 1886. Springer, pp 208–218 – reference: DalamagasTChengTWinkelK-JSellisTA methodology for clustering XML documents by structureInform Syst20063118722810.1016/j.is.2004.11.0091128.68345 – reference: WangLHassanzadehOZhangSShiJJiaoLZouJWangCSchema management for document storesProc VLDB Endow20158992293310.14778/2777598.2777601 – reference: BaaderFCalvaneseDMcGuinnessDNardiDPatel-SchneiderPThe description logic handbook2003CambridgeCambridge University Press1274.68451 – reference: Moh D-H, Lim E-P, Ng W-K (2000) Re-engineering structures from Web documents. In: 5th ACM conference on digital libraries (DL 2000). 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| SubjectTerms | Algorithms Artificial Intelligence Automatic data collection systems Classificació automàtica Computer Science Data mining Database Management Design Document Information Storage and Retrieval Information Systems Applications (incl.Internet) Informàtica IT in Business Mineria de dades Original Article Sistemes d'informació XML Àrees temàtiques de la UPC |
| Title | Approximating the Schema of a Set of Documents by Means of Resemblance |
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