Abstract
Keyword search is a popular technique for querying the ever increasing repositories of RDF graph data because it frees the user from knowing a formal query language and the structure of the data. However, the imprecision of keyword queries results in overwhelming numbers of candidate results making the identification of relevant results challenging and hindering the scalability of the query evaluation algorithms.
To address these issues, we introduce cohesive keyword queries on RDF data. Cohesive queries allow the user to flexibly and effortlessly convey her intention using cohesive keyword groups. A cohesive group of keywords in a query indicates that the keywords of the group should form a cohesive unit in the query results. We provide formal semantics of cohesive queries. We design a query evaluation algorithm which relies on the structural summary of the RDF graph to generate pattern graphs that satisfy the cohesiveness constraints. Pattern graphs are structured queries that can be evaluated over the RDF data to compute the query results. Our experiments demonstrate the efficiency of our algorithm and the effectiveness of cohesive keyword queries in improving the result quality and in pruning the space of pattern graphs compared to flat keyword queries. Most importantly, these benefits are achieved while retaining the simplicity and convenience of traditional keyword search.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Aksoy, C., Dass, A., Theodoratos, D., Wu, X.: Clustering query results to support keyword search on tree data. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 213–224. Springer, Heidelberg (2014)
Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., Sudarshan, S.: Keyword searching and browsing in databases using BANKS. In: ICDE, pp. 431–440 (2002)
Dass, A., Aksoy, C., Dimitriou, A., Theodoratos, D.: Exploiting semantic result clustering to support keyword search on linked data. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014, Part I. LNCS, vol. 8786, pp. 448–463. Springer, Heidelberg (2014)
Dass, A., Aksoy, C., Dimitriou, A., Theodoratos, D.: Keyword pattern graph relaxation for selective result space expansion on linked data. In: Cimiano, P., Frasincar, F., Houben, G.-J., Schwabe, D. (eds.) ICWE 2015. LNCS, vol. 9114, pp. 287–306. Springer, Heidelberg (2015)
Dimitriou, A., Dass, A., Theodoratos, D.: Cohesiveness relationships to empower keyword search on tree data on the web (2015). arXiv preprint arXiv:submit/1331603
Ding, B., Yu, J.X., Wang, S., Qin, L., Zhang, X., Lin, X.: Finding top-k min-cost connected trees in databases. In: ICDE, pp. 836–845 (2007)
Elbassuoni, S., Ramanath, M., Schenkel, R., Weikum, G.: Searching RDF graphs with SPARQL and keywords. IEEE Data Eng. Bull. 33, 16–24 (2010)
Fu, H., Gao, S., Anyanwu, K.: Disambiguating keyword queries on RDF databases using “Deep" segmentation. In: ICSC, pp. 236–243 (2010)
Golenberg, K., Kimelfeld, B., Sagiv, Y.: Keyword proximity search in complex data graphs. In: SIGMOD, pp. 927–940 (2008)
Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: XRANK: ranked keyword search over XML documents. In: SIGMOD, pp. 16–27 (2003)
He, H., Wang, H., Yang, J., Yu, P.S.: Blinks: ranked keyword searches on graphs. In: SIGMOD, pp. 305–316 (2007)
Jiang, M., Chen, Y., Chen, J., Du, X.: Interactive predicate suggestion for keyword search on RDF graphs. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS, vol. 7121, pp. 96–109. Springer, Heidelberg (2011)
Kacholia, V., Pandit, S., Chakrabarti, S., Sudarshan, S., Desai, R., Karambelkar, H.: Bidirectional expansion for keyword search on graph databases. In: VLDB, pp. 505–516 (2005)
Kargar, M., An, A.: Keyword search in graphs: finding r-cliques. VLDB 4, 681–692 (2011)
Le, W., Li, F., Kementsietsidis, A., Duan, S.: Scalable keyword search on large RDF data. IEEE Trans. Knowl. Data Eng. 26(11), 2774–2788 (2014)
Li, G., Ooi, B.C., Feng, J., Wang, J., Zhou, L.: Ease: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In: SIGMOD, pp. 903–914 (2008)
Li, X., Li, C., Yu, C.: Entity-relationship queries over Wikipedia. ACM TIST 3(4), 70 (2012)
Liu, X., Wan, C., Chen, L.: Returning clustered results for keyword search on XML documents. IEEE Trans. Knowl. Data Eng. 23(12), 1811–1825 (2011)
Pound, J., Ilyas, I.F., Weddell, G.E.: Expressive and flexible access to web-extracted data: a keyword-based structured query language. In: ACM SIGMOD, pp. 423–434 (2010)
Qin, L., Yu, J.X., Chang, L., Tao, Y.: Querying communities in relational databases. In: ICDE, pp. 724–735 (2009)
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, pp. 405–416 (2009)
Wang, H., Zhang, K., Liu, Q., Tran, T., Yu, Y.: Q2Semantic: a lightweight keyword interface to semantic search. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 584–598. Springer, Heidelberg (2008)
Xu, K., Chen, J., Wang, H., Yu, Y.: Hybrid graph based keyword query interpretation on RDF. In: ISWC (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dass, A., Dimitriou, A., Aksoy, C., Theodoratos, D. (2015). Incorporating Cohesiveness into Keyword Search on Linked Data. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-26187-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26186-7
Online ISBN: 978-3-319-26187-4
eBook Packages: Computer ScienceComputer Science (R0)