Abstract
Information access over linked data requires to determine subgraph(s), in linked data’s underlying graph, that correspond to the required information need. Usually, an information access framework is able to retrieve richer information by checking of a large number of possible subgraphs. However, on the fly checking of a large number of possible subgraphs increases information access complexity. This makes an information access frameworks less effective. A large number of contemporary linked data information access frameworks reduce the complexity by introducing different heuristics but they suffer on retrieving richer information. Or, some frameworks do not care about the complexity. However, a practically usable framework should retrieve richer information with lower complexity. In linked data information access, we hypothesize that pre-processed data statistics of linked data can be used to efficiently check a large number of possible subgraphs. This will help to retrieve comparatively richer information with lower data access complexity. Preliminary evaluation of our proposed hypothesis shows promising performance.
Keywords
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References
Aggarwal, N., Buitelaar, P.: A system description of natural language query over dbpedia. In: Proceedings of Interacting with Linked Data, pp. 96–99 (2012)
Covington, M.A.: A dependency parser for variable-word-order languages. In: Derohanes (eds.) Computer Assisted Modeling on the IBM 3090, pp.799–845 (1992)
Damljanovic, D., Agatonovic, M., Cunningham, H.: FREyA: An interactive way of querying linked data using natural language. In: Proceedings of the 1st Workshop on Question Answering over Linked Data, pp. 125–138 (2011)
Delbru, R., Toupikov, N., Catasta, M., Tummarello, G.: A node indexing scheme for web entity retrieval. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 240–256. Springer, Heidelberg (2010)
Elbassuoni, S., Blanco, R.: Keyword search over rdf graphs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 237–242 (2011)
Ferr, S.: squall2sparql: a Translator from Controlled English to Full SPARQL 1.1. Working Notes for CLEF 2013 Conference (2013)
Freitas, A., Oliveira, J., O’Riain, S., Curry, E., Pereira da Silva, J.: Treo: best-effort natural language queries over linked data. In: Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems, pp. 286–289 (2011)
Gangemi, A., Presutti, V.: Towards a pattern science for the semantic web. Semantic Web 1(1–2), 61–68 (2010)
Guyonvarch, J., Ferr, S., Ducass, M.: Scalable Query-based Faceted Search on top of SPARQL Endpoints for Guided and Expressive Semantic Search. Research report PI-2009, LIS - IRISA, October 2013
Kaufmann, E., Bernstein, A., Fischer, L.: NLP-Reduce: A nave but domain-independent natural language interface for querying ontologies. In: Proceedings of the 4th European Semantic Web Conference (2007)
Lopez, V., Motta, E., Uren, V.S.: PowerAqua: Fishing the semantic web. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 393–410. Springer, Heidelberg (2006)
Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press (2009)
Niu, Z., Zheng, H.-T., Jiang, Y., Xia, S.-T., Li, H.-Q.: Keyword proximity search over large and complex rdf database. In: Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 467–471 (2012)
Nuzzolese, A.G., Gangemi, A., Presutti, V., Ciancarini, P.: Encyclopedic knowledge patterns from wikipedia links. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 520–536. Springer, Heidelberg (2011)
Picalausa, F., Luo, Y., Fletcher, G.H.L., Hidders, J., Vansummeren, S.: A structural approach to indexing triples. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 406–421. Springer, Heidelberg (2012)
Rahoman, M.-M., Ichise, R.: An automated template selection framework for keyword query over linked data. In: Takeda, H., Qu, Y., Mizoguchi, R., Kitamura, Y. (eds.) JIST 2012. LNCS, vol. 7774, pp. 175–190. Springer, Heidelberg (2013)
Rahoman, M.-M., Ichise, R.: Automatic inclusion of semantics over keyword-based linked data retrieval. IEICE Transactions of Information and Systems E97-D(11) (2014)
He, S., Liu, S., Chen, Y., Zhou, G., Liu, K., Zhao, J.: CASIA@QALD-3: A Question Answering System over Linked Data. Working Notes for CLEF 2013 Conference (2013)
Unger, C., Bühmann, L., Lehmann, J., Ngomo, A.-C. N., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In Proceedings of the 21st World Wide Web Conference, pp. 639–648 (2012)
Zenz, G., Zhou, X., Minack, E., Siberski, W., Nejdl, W.: From keywords to semantic queries-incremental query construction on the semantic web. Journal of Web Semantics 7(3), 166–176 (2009)
Zhang, Z., Gentile, A.L., Blomqvist, E., Augenstein, I., Ciravegna, F.: Statistical knowledge patterns: Identifying synonymous relations in large linked datasets. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 703–719. Springer, Heidelberg (2013)
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Rahoman, MM., Ichise, R. (2015). inteSearch: An Intelligent Linked Data Information Access Framework. In: Supnithi, T., Yamaguchi, T., Pan, J., Wuwongse, V., Buranarach, M. (eds) Semantic Technology. JIST 2014. Lecture Notes in Computer Science(), vol 8943. Springer, Cham. https://doi.org/10.1007/978-3-319-15615-6_12
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DOI: https://doi.org/10.1007/978-3-319-15615-6_12
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