The vocabulary problem in human-system communication
Communications of the ACM
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
FleXPath: flexible structure and full-text querying for XML
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Efficient keyword search for smallest LCAs in XML databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Versatile structural disambiguation for semantic-aware applications
Proceedings of the 14th ACM international conference on Information and knowledge management
Keyword Proximity Search in XML Trees
IEEE Transactions on Knowledge and Data Engineering
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Query relaxation using malleable schemas
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Précis: from unstructured keywords as queries to structured databases as answers
The VLDB Journal — The International Journal on Very Large Data Bases
Querying complex structured databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Querying graphs with uncertain predicates
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
SAPPER: subgraph indexing and approximate matching in large graphs
Proceedings of the VLDB Endowment
A semantic-based approach for data management in a P2P system
Transactions on large-scale data- and knowledge-centered systems III
Answering subgraph queries over large graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Searching web data: An entity retrieval and high-performance indexing model
Web Semantics: Science, Services and Agents on the World Wide Web
Query languages for graph databases
ACM SIGMOD Record
Malleability-Aware skyline computation on linked open data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
SQBC: An efficient subgraph matching method over large and dense graphs
Information Sciences: an International Journal
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The largeness and the heterogeneity of most graph-modeled datasets in several database application areas make the query process a real challenge because of the lack of a complete knowledge of the vocabulary used, as well as of the information about the structural relationships between the data. To overcome these problems, flexible query answering capabilities are an essential need. In this paper we present a general model for supporting approximate queries on graph-modeled data. Approximation is both on the vocabularies and the structure. The model is general in that it is not bound to a specific graph data model, rather it gracefully accommodates labeled directed/undirected data graphs with labeled/unlabeled edges. The query answering principles underlying the model are not compelled to a specific data graph, instead they are founded on properties inferable from the data model the data graph conforms to. We complement the work with a ranking model to deal with data approximations and with an efficient top-k retrieval algorithm which smartly accesses ad-hoc data structures and generates the most promising answers in an order correlated with the ranking measures. Experimental results prove the good effectiveness and efficiency of our proposal on different real world datasets.