Approximate querying of RDF graphs via path alignment
- 384 Downloads
- 3 Citations
Abstract
A query over RDF data is usually expressed in terms of matching between a graph representing the target and a huge graph representing the source. Unfortunately, graph matching is typically performed in terms of subgraph isomorphism, which makes semantic data querying a hard problem. In this paper we illustrate a novel technique for querying RDF data in which the answers are built by combining paths of the underlying data graph that align with paths specified by the query. The approach is approximate and generates the combinations of the paths that best align with the query. We show that, in this way, the complexity of the overall process is significantly reduced and verify experimentally that our framework exhibits an excellent behavior with respect to other approaches in terms of both efficiency and effectiveness.
Keywords
Path Graph RDF Approximate matching AlignmentReferences
- 1.De Virgilio, R., Giunchiglia, F., Tanca, L. (eds.): Semantic Web Information Management—A Model-Based Perspective. Springer, Berlin (2010)MATHGoogle Scholar
- 2.De Virgilio, R., Guerra, F., Velegrakis, Y. (eds.): Semantic Search Over the Web. Springer, Berlin, Heidelberg (2012)Google Scholar
- 3.De Virgilio, R., Orsi, G., Tanca, L., Torlone, R.: Nyaya: A system supporting the uniform management of large sets of semantic data. In: ICD., pp. 1309–1312. (2012)Google Scholar
- 4.Bröcheler, M., Pugliese, A., Subrahmanian, V.S.: Dogma: A disk-oriented graph matching algorithm for rdf databases. In: ISWC, pp. 97–113. (2009)Google Scholar
- 5.Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: from intractable to polynomial time. Proc. VLDB Endow. 3(1), 264–275 (2010)CrossRefGoogle Scholar
- 6.Zhang, S., Yang, J., Jin, W.: Sapper: subgraph indexing and approximate matching in large graphs. Proc. VLDB Endow. 3(1), 1185–1194 (2010)CrossRefMATHGoogle Scholar
- 7.Wood, P.T.: Query languages for graph databases. SIGMOD Rec. 41(1), 50–60 (2012)CrossRefGoogle Scholar
- 8.Gallagher, B.: Matching structure and semantics : A survey on graph-based pattern matching. In: Artificial Intelligence, pp. 45–53. (2006)Google Scholar
- 9.Zhang, S., Li, S., Yang, J.: Gaddi: distance index based subgraph matching in biological networks. In: EDBT, pp. 192–203. (2009)Google Scholar
- 10.Khan, A., Li, N., Yan, X., Guan, Z., Chakraborty, S., Tao, S.: Neighborhood based fast graph search in large networks. In: SIGMOD, pp. 901–912. (2011)Google Scholar
- 11.Iordanov, B.: Hypergraphdb: A generalized graph database. In: WAIM Workshops, pp. 25–36. (2010)Google Scholar
- 12.Fellbaum, C. (ed.): WordNet An Electronic Lexical Database. The MIT Press, Cambridge (1998)Google Scholar
- 13.Hassanzadeh, O., Consens, M.P.: Linked movie data base (triplification challenge report). In: I-SEMANTICS, pp. 194–196 (2008)Google Scholar
- 14.Bizer, C., Schultz, A.: The Berlin sparql benchmark. Int. J. Semant. Web. Inf. Syst. 5(2), 1–24 (2009)CrossRefGoogle Scholar
- 15.Guo, Y., Pan, Z., Heflin, J.: Lubm: a benchmark for owl knowledge base systems. J. Web. Semant. 3(2–3), 158–182 (2005)CrossRefGoogle Scholar
- 16.Ma, L., Yang, Y., Qiu, Z., Xie, G.T., Pan, Y., Liu, S.: Towards a complete owl ontology benchmark. In: ESWC, pp. 125–139. (2006)Google Scholar
- 17.Cappellari, P., De Virgilio, R., Maccioni, A., Roantree, M.: A path-oriented rdf index for keyword search query processing. In: DEXA, pp. 366–380. (2011)Google Scholar
- 18.Zou, L., Chen, L., Özsu, M.T.: Distance-join: pattern match query in a large graph database. Proc. VLDB Endow. 2(1), 886–897 (2009)CrossRefGoogle Scholar
- 19.Fan, W., Bohannon, P.: Information preserving xml schema embedding. ACM Trans. Database Syst. 33(1) (2008)Google Scholar
- 20.Tran, T., Wang, H., Rudolph, S., Cimiano, P.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (rdf) data. In: ICDE Conference, pp. 405–416 (2009)Google Scholar
- 21.Neumann, T., Weikum, G.: x-rdf-3x: fast querying, high update rates, and consistency for rdf databases. Proc. VLDB Endow. 3(1), 256–263 (2010)CrossRefMATHGoogle Scholar
- 22.Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach. In: SIGMOD, pp. 335–346. (2004)Google Scholar
- 23.Zhang, S., Hu, M., Yang, J.: Treepi: A novel graph indexing method. In: ICDE, pp. 966–975. (2007)Google Scholar
- 24.Cheng, J., Ke, Y., Ng, W., Lu, A.: Fg-index: towards verification-free query processing on graph databases. In: SIGMOD, pp. 857–872. (2007)Google Scholar
- 25.Tian, Y., Patel, J.M.: Tale: A tool for approximate large graph matching. In: ICDE, pp. 963–972. (2008)Google Scholar
- 26.Zeng, Z., Tung, A.K.H., Wang, J., Feng, J., Zhou, L.: Comparing stars: on approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009)CrossRefGoogle Scholar
- 27.Jin, R., Xiang, Y., Ruan, N., Fuhry, D.: 3-hop: a high-compression indexing scheme for reachability query. In: SIGMOD, pp. 813–826. (2009)Google Scholar
- 28.Poulovassilis, A., Wood, P.T.: Combining approximation and relaxation in semantic web path queries. In: ISWC, pp. 631–646. (2010)Google Scholar
- 29.Chan, E.P.F., Lim, H.: Optimization and evaluation of shortest path queries. VLDB J. 16(3), 343–369 (2007)CrossRefMATHGoogle Scholar
- 30.Hu, W., Jian, N., Qu, Y., Wang, Y.: Gmo: A graph matching for ontologies. In: Integrating Ontologies. (2005)Google Scholar