ABSTRACT
RDF and RDFS have recently become very popular as frameworks for representing data and meta-data in form of a domain description, respectively. RDF data can also be thought of as graph data. In this paper, we focus on keyword-based querying of RDF data. In the existing approaches for answering such keyword queries, keywords are mapped to nodes in the graph and their neighborhoods are explored to extract subgraph(s) of the data graph that contain(s) information relevant to the query. In order to restrict the computational effort, a fixed distance bound is used to define the neighborhoods of nodes. In this paper we present an elegant algorithm for keyword query processing on RDF data that does not assume such a fixed bound. The approach adopts a pruned exploration mechanism where closely related nodes are identified, subgraphs are pruned and joined using suitable hook nodes. The system dynamically manages the distance depending on the closeness between the keywords. The working of the algorithm is illustrated using a fragment of AIFB institute data represented as an RDF graph.
- Allemang, D., and Hendler, J. Semantic Web for the Working Ontologist Modeling in RDF, RDFS and OWL. Morgan Kaufmann Publishers, Reading, Massachusetts, 2008. Google ScholarDigital Library
- B. Kimfield, and Y. Sagir. Finding and approximating top-k answers in keyword proximity search. In PODS 2006 (2006), ACM, pp. 173--182. Google ScholarDigital Library
- G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakraborti, and S. Sudharshan. Keyword searching and browsing in database using banks. In ICDE 2002 (2002), ACM, pp. 431--440. Google ScholarDigital Library
- H. He, H. Wang, J. Yang, and P. S. Yu. Blinks: Ranked keyword searches on graphs. In SIGMOD Conference 2007 (2007), ACM, pp. 305--316. Google ScholarDigital Library
- Kasneci, G., Ramanath, M., Sozio, M., Suchanek, F., and Weikum, G. Star: Steiner tree approximation in relationship graphs. In 25th IEEE International Conference on Data Engineering, ICDE 2009 (2009), IEEE, pp. 868--879. Google ScholarDigital Library
- K. Parthasarathy, P. Sreenivasa Kumar, and Damien, D. Answer graph construction for keyword search on graph structured (rdf) data. In International Conference on Knowledge Discovery and Information Retrieval(KDIR) 2010 (Oct 2010), INSTICC.Google Scholar
- Lei, Y., Uren, V., and Molta, E. Semsearch: A search engine for the semantic web. In 15th International Conference on Knowledge Engineering and Knowledge Management (EKAW), (2006) (2006), pp. 238--245. Google ScholarDigital Library
- L. Guo, F. Shao, C. Botev, and J. Shanmugasundaram. Xrank: Ranked keyword search over xml documents. In SIGMOD Conference 2003 (2003), ACM, pp. 16--27. Google ScholarDigital Library
- Li, G., Ooi, B. C., Feng, J., Wang, J., and Zhou, L. Ease: An effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In SIGMOD 2008 (2008), ACM, pp. 1452--1455. Google ScholarDigital Library
- Q. Zhou, C. Wang, M. Xiong, H. Wang, and Y. Yu. Spark: Adapting keyword query to semantic search. In ISWC/ASWC, 2007 (2007), SWSA, pp. 694--707. Google ScholarDigital Library
- Revuri, S., Upadhyaya, S., and P. Sreenivasa Kumar. Using domain ontologies for efficient information retrieval. In International Conference on Management of Data COMAD 2006 (Dec 2006), CSI, pp. 84--89.Google Scholar
- T. Tran, P. Camiano, S. Rudolph, and R. Studer. Ontology based interpretation of keywords for semantic search. In ISWC/ASWC, 2007 (2007), SWSA, pp. 523--536. Google ScholarDigital Library
- V. Kacholia, S. Pandit, S. Chakraborti, S. Sudharshan, R. Desai, and H. Karambelkar. Bidirectional expansion for keyword search on graph databases. In VLDB 2005 (2005), VLDB, pp. 505--516. Google ScholarDigital Library
- Y. Cai, X. Dong, A. Halevy, J. Liu, and J. Madhavan. Personal information management with semex. In SIGMOD 2005 (2005), ACM, pp. 921--923. Google ScholarDigital Library
- Y. Sure, S. Bloehdorn, P. Haase, J. Hartmann, and D. Oberle. The swrc ontology - semantic web for research communities. In In Proceedings of the 12th Portuguese Conference on AI (EPIA 2005) (2005), ECCAI, pp. 218--231. Google ScholarDigital Library
Index Terms
- Ranked answer graph construction for keyword queries on RDF graphs without distance neighbourhood restriction
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