Optimizing ranking functions: a connectionist approach to adaptive information retrieval
Optimizing ranking functions: a connectionist approach to adaptive information retrieval
RDF123: From Spreadsheets to RDF
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Unlocking the potential of public sector information with semantic web technology
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Communications of the ACM
TWC LOGD: A portal for linked open government data ecosystems
Web Semantics: Science, Services and Agents on the World Wide Web
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
A publishing pipeline for linked government data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
QuerioCity: a linked data platform for urban information management
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Searching in the city of knowledge: challenges and recent developments
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Governments and enterprises are interested in the return-on-investment for exposing their data. This brings forth the problem of making data consumable, with minimal effort. Beyond search techniques, there is a need for effective methods to identify heterogeneous datasets that are closely related, as part of data integration or exploration tasks. The large number of datasets demands a new generation of Smarter Systems for data content aggregation that allows users to incrementally liberate, access and integrate information, in a manner that scales in terms of gain for the effort spent. In the context of such a pay-as-you go system, we are presenting a novel method for exploring and discovering relevant datasets based on semantic relatedness. We are demonstrating a system for contextual knowledge mining on hundreds of real-world datasets from Dublin City. We evaluate our semantic approach, using query logs and domain expert judgments, to show that our approach effectively identifies related datasets and outperforms text-based recommendations.