Discovering interesting information with advances in web technology

Abstract

The Web is a steadily evolving resource comprising much more than mere HTML pages. With its ever-growing data sources in a variety of formats, it provides great potential for knowledge discovery. In this article, we shed light on some interesting phenomena of the Web: the deep Web, which surfaces database records as Web pages; the Semantic Web, which defines meaningful data exchange formats; XML, which has established itself as a lingua franca for Web data exchange; and domain-specific markup languages, which are designed based on XML syntax with the goal of preserving semantics in targeted domains. We detail these four developments in Web technology, and explain how they can be used for data mining. Our goal is to show that all these areas can be as useful for knowledge discovery as the HTML-based part of the Web.

References

1. Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web: From Relations to Semistructured Data and XML. California: Morgan Kaumann, 2000.
2. Aggarwal, C. C., Ta, N., Wang, J., Feng, J., Zaki, M.J: Xproj: a framework for projected structural clustering of XML documents. In Proc. KDD, pp. 46--55, 2007.
3. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In Proc. SIGMOD, pp. 207--216, 1993.
4. Antoniou, G., F. van Harmelen. A Semantic Web Primer (Cooperative Information Systems). The MIT Press, April 2004.
5. Arasu, A., Garcia-Molina, H.: Extracting structured data from Web pages. In Proc. SIGMOD, pp. 337--348, 2003.
6. Auer, S., Bizer, C., Lehmann, J., Kobilarov, G., Cyganiak, R., Ives, Z.: DBpedia: A Nucleus for a Web of Open Data. In Proc. ISWC, pp. 722--735, 2007.
7. Aumueller, D., Do, H. H., Massmann, S., Rahm, E. Schema and ontology matching with COMA++. In Proc. SIGMOD, pages 906--908, 2005.
8. Böhm, C., de Melo, G., Naumann, F., Weikum, G.: LINDA: Distributed web-of-data-scale entity matching. In Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM), New York, NY, USA, 2012. ACM.
9. Barbosa, L., Freire, J.: Searching for hidden-Web databases. In Proc. WebDB, pp. 1--6, 2005.
10. Begley, E.F.: MatML Version 3.0 Schema. NIST 6939, National Institute of Standards and Technology Report, USA, Jan 2003.
11. Bhattacharya, I., Getoor, L. Collective entity resolution in relational data. ACM TKDD, 1, 03 2007.
12. Bizer, C., Heath, T., Idehen, K., Berners-Lee, T.: Linked data on the web (LDOW2008). In Proc. WWW, 2008, pp. 1265--1266.
13. Bleiholder, J., Naumann, F. Data fusion. ACM Computing Surveys, 41(1), 2008.
14. Boag, S., Fernandez, M., Florescu, D., Robie J., Simeon, J.: XQuery 1.0: An XML Query Language. W3C Working Draft, Nov 2003.
15. Bray, T., Hollander, D., Layman, A., Tobin, R.: Namespaces in XML 1.0 (Second Edition). W3C Recommendation, Aug 2006.
16. BrightPlanet: The deep Web: Surfacing hidden value. White Paper, Jul 2000.
17. Carlisle, D., Ion, P., Miner, R., Poppelier, N.: Mathematical Markup Language (MathML). WorldWideWeb Consortium, 2001.
18. Caverlee, J., Liu, L., Buttler, D.: Probe, cluster, and discover: Focused extraction of qa-pagelets from the deep Web. In Proc. ICDE, pp. 103--115, 2004.
19. Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: A new approach to topic-specific Web resource discovery. Computer Networks, 31(11--16):1623--1640, 1999.
20. Charles, L., Clarke, A., Craswell, N., Soboroff, I., and Voorhees, E.: Overview of the TREC Web Track, 2011.
21. Chi, Y., Nijssen, S., Muntz, R.R., Kok, J.N.: Frequent subtree mining -- an overview. Fundamenta Informaticae, 66(1-2):161--228, 2005.
22. Chuang, S.L., Chang, K.C., Zhai, C.: Context-aware wrapping: Synchronized data extraction. In Proc. VLDB, pp. 699--710, 2007.
23. Chang, K.C., He, B., Zhang, Z.: Toward large scale integration: Building a metaquerier over databases on the Web. In Proc. CIDR, pp. 44--55, 2005.
24. Cimiano, P., Hotho, A., Staab., S.: Comparing Conceptual, Divisive and Agglomerative Clustering for Learning Taxonomies from Text. In ECAI '04. IOS Press, 2004.
25. Clark, J.: XSL Transformations (XSLT). W3C Recommendation, Nov 1999.
26. Clark, J., DeRose, S.: XML Path Language (XPath). W3C Recommendation, Nov 1999.
27. Crescenzi, V., Mecca, G., Merialdo, P.: Roadrunner: Towards automatic data extraction from large Web sites. In Proc. VLDB, pp. 109--118, 2001.
28. d'Amato, C., Fanizzi, N., Esposito, F.: Inductive learning for the Semantic Web: What does it buy? Semant. web, 1(1,2), Apr. 2010.
29. David, J., Guillet, F., Briand, H.: Association Rule Ontology Matching Approach. Int. J. Semantic Web Inf. Syst., 3(2), 2007.
30. Davidson, S., Fan, W., Hara, C., Qin, J.: Propagating XML Constraints to Relations. In Proc. ICDE, pp. 543--552, 2003.
31. Dehaspe, L., Toironen, H.: Discovery of relational association rules. In Relational Data Mining. Springer- Verlag New York, Inc., 2000.
32. Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Min. Knowl. Discov., 3(1), Mar. 1999.
33. Denoyer, L., Gallinari, P..: Report on the xml mining track at inex 2007. ACM SIGIR Forum, pp. 22--28, 2008.
34. Ding, L., Shinavier, J., Shangguan, Z., McGuinness D. L.: SameAs networks and beyond: Analyzing deployment status and implications of owl:sameAs in linked data. In Proc. ISWC, pages 142--147, 2010.
35. Elmagarmid, A., Ipeirotis, P., Verykios, V. Duplicate record detection: A survey. IEEE TKDE, 19(1):1--16, 2007.
36. Erik, W., Robert, J.: Xml fever. Communications of the ACM, 51(7):40--46, 2003.
37. Ferrara, A., Lorusso, D., Montanelli, S.: Automatic identity recognition in the semantic web. In Proc. IRSW, 2008.
38. Galarraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases. In WWW 2013, 2013.
39. Garofalakis, M.N., Gionis, A., Rastogi, R., Seshadri, S., Shim, K.: Xtract: A system for extracting document type descriptors from xml documents. In Proc. SIGMOD, pp. 165--176, 2000.
40. Glaser, H., Jaffri, A., Millard, I.: Managing co-reference on the semantic Web. In Proc. LDOW, 2009.
41. Goethals, B., Van den Bussche, J.: Relational Association Rules: Getting WARMER. In Pattern Detection and Discovery, volume 2447. Springer Berlin / Heidelberg, 2002.
42. Gracia, J., d'Aquin, M. Mena, E.: Large scale integration of senses for the semantic Web. In Proc. WWW, pages 611--620, 2009.
43. Grimnes, G.A., Edwards, P., Preece, A.D.: Learning Meta-descriptions of the FOAF Network. In ISWC'04, 2004.
44. Guo, J., Araki, K., Tanaka, K., Sato, J., Suzuki, M., Takada, A., Suzuki, T., Nakashima, Y., Yoshihara, H.: MML (Medical Markup Language) Version 2.3 - XML based Standard for Medical Data Exchange/Storage. Journal of Medical Systems, 27(4):357--366, 2003.
45. Halpin, H., Hayes, P., McCusker, J.P., McGuinness, D., Thompson, H.S.: When owl:sameAs isn't the same: An analysis of identity in linked data. In Proc. ISWC, pages 305--320, 2010.
46. He, B., Patel, M., Zhang, Z., Chang, K.C.: Accessing the deep Web: A survey. Communications of the ACM, 50(2):94--101, 2007.
47. Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL Class Descriptions on Very Large Knowledge Bases. Int. J. Semantic Web Inf. Syst., 5(2), 2009.
48. A. Hogan.: Performing object consolidation on the semantic Web data graph. In Proc. I3, 2007.
49. Hogan, A., Polleres, A., Umbrich, J., Zimmermann, A.: Some entities are more equal than others: statistical methods to consolidate linked data. In Proc. NeFoRS, 2010.
50. Horrocks, I., Patel-Schneider, P.: Reducing OWL entailment to description logic satisfiability. Web Semantics: Science, Services and Agents on the World Wide Web, 1(4):345--357, 2004.
51. Hu, W., Chen, J., Qu, Y.: A self-training approach for resolving object coreference on the semantic Web. In Proc. WWW, pages 87--96, 2011.
52. Hu, w., Chen, J., Zhang, H., Qu, Y.: How matchable are four thousand ontologies on the semantic Web. In Proc. ESWC, pages 290--304, 2011.
53. Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In Proc. ICDM, 2003.
54. Inokuchi, A., Washio, T., Motoda, H.: A general framework for mining frequent subgraphs from labeled graphs. Fundamenta Informaticae, 66(1-2):53--82, 2005.
55. Ipeirotis, P.G., Gravano, L.: Distributed search over the hidden Web: Hierarchical database sampling and selection. In Proc. VLDB, pp. 394--405, 2002.
56. Isaac, A., van der Meij, L., Schlobach, S., Wang, S.: An empirical study of instance-based ontology matching. In Proc. ISWC, pages 253--266, 2007.
57. Jean-Mary, Y., Shironoshita, E., Kabuka, M.: Ontology matching with semantic verification. J. Web Semantics, 7(3):235--251, 2009.
58. Jiang, T., Tan, A.H., Wang, K.: Mining Generalized Associations of Semantic Relations from Textual Web Content. IEEE Trans. Knowl. Data Eng., 19(2), 2007.
59. Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. Theory Pract. Log. Program., 10(3), 2010.
60. Kuramochi, M., Karypis, G.: Frequent Subgraph Discovery. In ICDM '01. IEEE Computer Society, 2001.
61. Kutty, S., Nayak, R.: Clustering xml documents using closed frequent subtrees -- a structural similarity approach. In 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX, pp. 183--194, 2008.
62. Kutty, S., Nayak, R.: Frequent pattern mining on xml documents. Chapter 14 In Handbook of Research on Text and Web Mining Technologies, pp. 227-248, Idea Group Inc., USA, 2008.
63. Landauer, T.K., Foltz, P.N., Laham, D.: An introduction to latent semantic analysis. Discourse Processes, (25):259--284, 1998.
64. Lee, D., Sebastian Seung, H.: Algorithms for nonnegative matrix factorization. In Advances in Neural Information Processing Systems 13, pp. 556--562, 2000.
65. Lehmann, J.: DL-Learner: Learning Concepts in Description Logics. Journal of Machine Learning Research (JMLR), 10, 2009.
66. Li, J., Tang, J., Li, Y., and Luo, Q.: Rimom: A dynamic multistrategy ontology alignment framework. IEEE TKDE, 21(8):1218--1232, 2009.
67. Madhavan, J., Halevy, A.Y., Cohen, S., Dong, X., Jeffery, S.R., Ko, D., Yu, C.: Structured data meets the Web: A few observations. IEEE Data Engineering Bulletin, 29(4):19--26, 2006.
68. Maedche, A., Staab, S.: Discovering Conceptual Relations from Text. In ECAI'00, 2000.
69. Maedche, A., Zacharias, V.: Clustering Ontology-Based Metadata in the Semantic Web. In PKDD '02, 2002.
70. McGuinness, D.L., Fikes, R., Rice, J., Wilder, S.: An Environment for Merging and Testing Large Ontologies. In KR2000, 2000.
71. Muggleton, S.: Inverse entailment and progol. New Generation Comput., 13(3&4), 1995.
72. Murray-Rust, P., Rzepa, H.S.: Chemical Markup, XML, and the Worldwide Web Basic Principles. Journal of Chemical Informatics and Computer Science, 39:928--942, 1999.
73. Nayak, R.: Fast and effective clustering of XML data using structural information. Knowledge and Information Systems, 14(2):197--215, 2008.
74. Nayak, R.: XML data mining: Process and applications. Chapter 15 In Handbook of Research on Text and Web Mining Technologies, pp. 249 - 272, Idea Group Inc., USA, 2008.
75. Nayak, R., Iryadi, W.: XML schema clustering with semantic and hierarchical similarity measures. Knowledge-based Systems, 20:336--349, 2007.
76. Nayak, R., Tran., T: A progressive clustering algorithm to group the XML data by structural and semantic similarity. International Journal of Pattern Recognition and Artificial Intelligence, 21(4):723--743, 2007.
77. Nayak, R., and Zaki, M.J. Knowledge Discovery from XML documents: PAKDD 2006 Workshop Proceedings, volume 3915, 2006.
78. Network Working Group. Uniform Resource Identifier (URI): Generic Syntax, 2005. http://tools.ietf.org/html/rfc3986.
79. Nebot, V., Berlanga, R.: Finding association rules in semantic web data. Knowl.-Based Syst., 25(1), 2012.
80. Nebot, V., Llavorim, R.B.: Mining Association Rules from Semantic Web Data. In IEA/AIE (2), 2010.
81. Noessner, J., Niepert, M., Meilicke, C., and Stuckenschmidt, H.: Leveraging terminological structure for object reconciliation. In Proc. ESWC, pages 334--348, 2010.
82. Noy, N.F., Musen, M.A.: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In AAAI/IAAI '00. AAAI Press, 2000.
83. Raghavan, S., Garcia-Molina, H.: Crawling the hidden Web. In Proc. VLDB, pp. 129--138, 2001.
84. Ru, Y., Horowitz, E.: Indexing the invisible web: a survey. Online Information Review, 29(3):249--265, 2009.
85. Saïs, F., Pernelle, N., Rousset, M.C.: L2R: A logical method for reference reconciliation. In Proc. AAAI, pages 329--334, 2007.
86. Saïs, F., Pernelle, N., Rousset, M.C.: Combining a logical and a numerical method for data reconciliation. J. Data Semantics, 12:66--94, 2009.
87. Salton, G., McGill, M.J.: Introduction to Modern information Retrieval. McGraw-Hill, 1983.
88. Schoenmackers, S., Etzioni, O., Weld, D.S., Davis, J.: Learning first-order Horn clauses from web text. In EMNLP '10. Association for Computational Linguistics, 2010.
89. Senellart, P.: Comprendre le Web caché. Understanding the Hidden Web. PhD thesis, Université Paris XI, Orsay, France, December 2007.
90. Senellart, P., Mittal, A., Muschick, D., Gilleron, R., Tommasi, M.: Automatic wrapper induction from hidden-Web sources with domain knowledge. In WIDM, pp. 9--16, 2008.
91. Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: Probabilistic Alignment of Relations, Instances, and Schema. PVLDB, 5(3):157--168, 2011.
92. Suchanek, F.M., Kasneci, G., Weikum, G: YAGO: A core of semantic knowledge. Unifying WordNet and Wikipedia. In Proc. WWW, pp. 697--706, 2007.
93. Talukdar, P.P., Wijaya, D., Mitchell, T.: Acquiring temporal constraints between relations. In CIKM'12, 2012.
94. Taylor, P. and Isard A.: SSML: A Speech Synthesis Markup Language. Speech Communication, 21(1-2):123--133, 1997.
95. Tran, T., Nayak, R., Bruza, P.: Combining structure and content similarities for xml document clustering. In AusDM, pp. 219--226, 2008.
96. Totten, G., Bates, C., Clinton, N.: Handbook of Quench Technology and Quenchants. ASM International, 1993.
97. Tummarello, G., Cyganiak, R., Catasta, M., Danielczyk, S., Delbru, R., Decker, R.: Sig.ma: live views on the web of data. In Proc. WWW, pp. 1301--1304, 2010.
98. Udrea, O., Getoor, L., Miller, R.J.: Leveraging data and structure in ontology integration. In Proc. SIGMOD, pp. 449--460, 2007.
99. Varde, A., Begley, E., Fahrenholz, S.: MatML: XML for Information Exchange with Materials Property Data Proc. ACM KDD DM-SSP Workshop, 2006.
100. Varde, A., Maniruzzaman, M., Sisson Jr., R.: QuenchML: A Semantics-Preserving Markup Language for Knowledge Representation in Quenching. AIEDAM Journal, Cambridge University Press, Volume 27, pp. 65--82, 2012.
101. Varde, A., Rundensteiner, E., Mani, M., Maniruzzaman, M., Sisson Jr., R.: Augmenting MatML with Heat Treating Semantics. ASM International's Symposium on Web-Based Materials Property Databases, 2004.
102. Varde A., Rundensteiner, E., Fahrenholz S.: XML Based Markup Languages for Specific Domains. Book Chapter to appear in Web Based Support Systems, Springer-Verlag, UK, pp. 215--238, 2010.
103. Villaverde, J., Persson, A., Godoy, D., Amandi, A.: Supporting the discovery and labeling of non-taxonomic relationships in ontology learning. Expert Systems with Applications, 36(7), 2009.
104. Völker, J., Niepert, M.: Statistical schema induction. In ESWC'11, 2011.
105. Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and maintaining links on the Web of data. In Proc. ISWC, pp. 650--665, 2009.
106. Wang, S., Englebienne, G., Schlobach, S.: Learning concept mappings from instance similarity. In Proc. ISWC, pp. 339--355, 2008.
107. World Wide Web Consortium. W3C Semantic Web Activity, 1994. http://www.w3.org/2001/sw/
108. World Wide Web Consortium. RDF Primer (W3C Recommendation 2004-02-10). http://www.w3.org/TR/rdf-primer/, 2004.
109. World Wide Web Consortium. RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation 2004-02-10.
110. RDF/XML Syntax Specification (Revised), W3C Recommendation, 2004. http://www.w3.org/TR/rdf-syntax-grammar/
111. World Wide Web Consortium. SPARQL Query Language for RDF (W3C Recommendation 2008-01-15). http://www.w3.org/TR/rdf-sparql-query/.
112. W3C. Web Services Glossary, February 2004. http://www.w3.org/TR/ws-gloss/
113. Wu, W. Doan, A., Yu, C.T., Meng, W.: Bootstrapping domain ontology for semantic Web services from source Web sites. In Technologies for E-Services, pp. 11--22, 2005.
114. World Wide Web Consortium. XML Schema Part 2: Datatypes Second Edition, 2004. http://www.w3.org/TR/xmlschema-2/
115. Yokota, K., Kunishima, T., Liu, B.: Semantic Extensions of XML for Advanced Applications. IEEE Australian Computer Science Communications Workshop on Information Technology for Virtual Enterprises, 23(6):49-57, 2001.
116. Zaki, M.J.: Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(8):1021--1035, 2005.
117. Zhong, C., Bakshi A. and Prasanna, V.: ModelML: a Markup Language for Automatic Model Synthesis. IEEE International Conference on Information Reuse and Integration pp. 317--342, 2007.

Index Terms

• Information systems

  • Information retrieval

    • Document representation

  • Information storage systems

  • Information systems applications

    • Data mining