Making large-scale support vector machine learning practical
Advances in kernel methods
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 16th international conference on World Wide Web
Dynamic personalized pagerank in entity-relation graphs
Proceedings of the 16th international conference on World Wide Web
Authority-based keyword search in databases
ACM Transactions on Database Systems (TODS)
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
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
Recipes for semantic web dog food: the ESWC and ISWC metadata projects
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
USEWOD2011: 1st international workshop on usage analysis and the web of data
Proceedings of the 20th international conference companion on World wide web
Repeatable and reliable search system evaluation using crowdsourcing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Effective and efficient entity search in RDF data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Hierarchical link analysis for ranking web data
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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The amount of structured data is growing rapidly. Given a structured query that asks for some entities, the number of matching candidate results is often very high. The problem of ranking these results has gained attention. Because results in this setting equally and perfectly match the query, existing ranking approaches often use features that are independent of the query. A popular one is based on the notion of centrality that is derived via PageRank. In this paper, we adopt learning to rank approach to this structured query setting, provide a systematic categorization of query-independent features that can be used for that, and finally, discuss how to leverage information in access logs to automatically derive the training data needed for learning. In experiments using real-world datasets and human evaluation based on crowd sourcing, we show the superior performance of our approach over two relevant baselines.