Harvesting, searching, and ranking knowledge on the web: invited talk
Proceedings of the Second ACM International Conference on Web Search and Data Mining
The YAGO-NAGA approach to knowledge discovery
ACM SIGMOD Record
Language-model-based ranking for queries on RDF-graphs
Proceedings of the 18th ACM conference on Information and knowledge management
MING: mining informative entity relationship subgraphs
Proceedings of the 18th ACM conference on Information and knowledge management
Automatically incorporating new sources in keyword search-based data integration
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discover hierarchical subgraphs with network-topology based ranking score
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Querying Wikipedia documents and relationships
Procceedings of the 13th International Workshop on the Web and Databases
A framework for evaluating database keyword search strategies
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Bayesian knowledge corroboration with logical rules and user feedback
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
ISReal: an open platform for semantic-based 3D simulations in the 3D internet
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
CoSi: context-sensitive keyword query interpretation on RDF databases
Proceedings of the 20th international conference companion on World wide web
Proceedings of the 20th international conference companion on World wide web
Fragmenting Steiner tree browsers based on Ajax
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Nearest keyword search in XML documents
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Keyword search over RDF graphs
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding information nebula over large networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to rank results in relational keyword search
Proceedings of the 20th ACM international conference on Information and knowledge management
Retrieving keyworded subgraphs with graph ranking score
Expert Systems with Applications: An International Journal
Ranking structural parameters for social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Fast approximation of steiner trees in large graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
A semantics driven user interface for virtual saarlouis
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
An extended compact TVP index for finding top-k nearest neighbors over XML data tree
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Incorporating compactness to generate term-association view snippets for ontology search
Information Processing and Management: an International Journal
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Large graphs and networks are abundant in modern information systems: entity-relationship graphs over relational data or Web-extracted entities, biological networks, social online communities, knowledge bases, and many more. Often such data comes with expressive node and edge labels that allow an interpretation as a semantic graph, and edge weights that reflect the strengths of semantic relations between entities. Finding close relationships between a given set of two, three, or more entities is an important building block for many search, ranking, and analysis tasks. From an algorithmic point of view, this translates into computing the best Steiner trees between the given nodes, a classical NP-hard problem. In this paper, we present a new approximation algorithm, coined STAR, for relationship queries over large relationship graphs. We prove that for n query entities, STAR yields an O(log(n))-approximation of the optimal Steiner tree in pseudopolynomial run-time, and show that in practical cases the results returned by STAR are qualitatively comparable to or even better than the results returned by a classical 2-approximation algorithm. We then describe an extension to our algorithm to return the top-k Steiner trees. Finally, we evaluate our algorithm over both main-memory as well as completely diskresident graphs containing millions of nodes. Our experiments show that in terms of efficiency STAR outperforms the best state-of-the-art database methods by a large margin, and also returns qualitatively better results.