Abstract
Relax NG is a popular and powerful schema language for XML, which concerns the relative order among the elements. Since many XML documents in practice either miss schemas or lack valid schemas, we focus on inferring a concise Relax NG from some XML documents. The fundamental task of Relax NG inference is learning regular expressions. Previous work in this direction lacks support of all operators allowed in Relax NG especially for interleaving. In this paper, by analysis of large-scale real-world Relax NG, we propose a restricted subclass of regular expressions called chain regular expressions with interleaving (ICREs). Meanwhile, we develop a learning algorithm to infer a descriptive generalized ICRE from XML samples, based on single occurrence automata and the maximum clique. We conduct experiments on real benchmark from DBLP. Experimental results show that ICREs are expressive enough to cover the vast majority of practical Relax NG. Our algorithm can effectively learn from small and large dataset, and our results are concise and more precise than other popular methods.
H. Chen—Work supported by the National Natural Science Foundation of China under Grant No. 61472405.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Where \(m_i\in \mathbb {N},n_i\in \mathbb {N}\setminus \{0\}\cup \{{\infty \}}\) and \(i=\{1,2\cdots \}.\).
- 2.
The content model of XSD must be deterministic expressions formally defined in [9]. So nondeterministic forms like \(((day^{?},month) | (month, day^{?}))^{?}, year\) are illegal.
- 3.
- 4.
The learning results are not unique, but GenICRE only returns one of the solutions.
- 5.
m is the length of the expression excluding operators, \(\emptyset \) and \(\varepsilon \) [4].
References
Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web: from Relations to Semistructured Data and XML. Morgan Kaufmann, Burlington (2000)
Barbosa, D., Mignet, L., Veltri, P.: Studying the XML web: gathering statistics from an XML sample. World Wide Web-Internet Web Inf. Syst. 9(2), 187–212 (2006)
Beek, M.H.T., Kleijn, J.: Infinite unfair shuffles and associativity. Theor. Comput. Sci. 380(3), 401–410 (2007)
Bex, G.J., Gelade, W., Neven, F., Vansummeren, S.: Learning deterministic regular expressions for the inference of schemas from XML data. ACM Trans. Web 4(4), 1–32 (2010)
Bex, G.J., Neven, F., Bussche, J.V.D.: DTDs versus XML schema: a practical study. In: International Workshop on the Web and Databases, pp. 79–84 (2004)
Bex, G.J., Neven, F., Schwentick, T., Vansummeren, S.: Inference of concise regular expressions and DTDs. ACM Trans. Database Syst. 35(2), 1–47 (2010)
Bex, G.J., Neven, F., Vansummeren, S.: Inferring XML schema definitions from XML data. In: International Conference on Very Large Data Bases, University of Vienna, Austria, September, pp. 998–1009 (2007)
Boneva, I., Ciucanu, R., Staworko, S.: Simple schemas for unordered XML. In: International Workshop on the Web and Databases (2015)
Brüggemann-Klein, A.: Unambiguity of extended regular expressions in SGML document grammars. In: Lengauer, T. (ed.) ESA 1993. LNCS, vol. 726, pp. 73–84. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-57273-2_45
Che, D., Aberer, K., Özsu, M.T.: Query optimization in XML structured-document databases. VLDB J. 15(3), 263–289 (2006)
Ciucanu, R., Staworko, S.: Learning schemas for unordered XML. Computer Science (2013)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn, p. 1297C1305 (2001)
Demany, D.: InstanceToSchema: a RELAX NG schema generator from XML instances (2003). http://www.xmloperator.net/i2s/
Feige, U.: Approximating maximum clique by removing subgraphs. SIAM J. Discret. Math. 18(2), 219–225 (2006)
Fernau, H.: Algorithms for learning regular expressions. Inf. Comput. 207(4), 521–541 (2009)
Florescu, D.: Managing semi-structured data. ACM Queue 3(8), 18–24 (2005)
Freydenberger, D.D., Kötzing, T.: Fast learning of restricted regular expressions and DTDs. Theory Comput. Syst. 57(4), 1114–1158 (2015)
Garcia, P., Vidal, E.: Inference of k-testable languages in the strict sense and application to syntactic pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(9), 920–925 (2002)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)
Garofalakis, M., Gionis, A., Shim, K., Shim, K., Shim, K.: XTRACT: learning document type descriptors from XML document collections. Data Mining Knowl. Discov. 7(1), 23–56 (2003)
Garofalakis, M.N., Gionis, A., Rastogi, R., Seshadri, S., Shim, K.: XTRACT: a system for extracting document type descriptors from XML documents. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, 16–18 May 2000, pp. 165–176 (2000)
Gold, E.M.: Language identification in the limit. Inf. Control 10(5), 447–474 (1967)
Grijzenhout, S., Marx, M.: The quality of the XML web. Web Semant.: Sci. Serv. Agents World Wide Web 19, 59–68 (2013)
Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation. Addison-Wesley Series in Computer Science, 2nd edn. Addison-Wesley-Longman, Boston (2001). ISBN: 978-0-201-44124-6
Clark, J., Murata, M.: Organization for the Advancement of Structured Information Standards (OASIS). Relax NG specification (2001)
Kim, G.-H., Ko, S.-K., Han, Y.-S.: Inferring a relax NG schema from XML documents. In: Dediu, A.-H., Janoušek, J., Martín-Vide, C., Truthe, B. (eds.) LATA 2016. LNCS, vol. 9618, pp. 400–411. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30000-9_31
Koch, C., Scherzinger, S., Schweikardt, N., Stegmaier, B.: Schema-based scheduling of event processors and buffer minimization for queries on structured data streams. In: Thirtieth International Conference on Very Large Data Bases, pp. 228–239 (2004)
Manolescu, I., Florescu, D., Kossmann, D.: Answering XML queries on heterogeneous data sources. In: International Conference on Very Large Data Bases, pp. 241–250 (2001)
Martens, W., Neven, F.: Typechecking top-down uniform unranked tree transducers. In: International Conference on Database Theory, pp. 64–78 (2003)
Martens, W., Neven, F.: Frontiers of tractability for typechecking simple XML transformations. In: ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 23–34 (2004)
Peng, F., Chen, H.: Discovering restricted regular expressions with interleaving. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) APWeb 2015. LNCS, vol. 9313, pp. 104–115. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25255-1_9
Quinlan, J.R., Rivest, R.L.: Inferring decision trees using the minimum description length principle. Inf. Comput. 80(3), 227–248 (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y., Mou, X., Chen, H. (2018). Learning Concise Relax NG Schemas Supporting Interleaving from XML Documents. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-05090-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05089-4
Online ISBN: 978-3-030-05090-0
eBook Packages: Computer ScienceComputer Science (R0)