Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2481492.2481524 | HT |
Keywords | Field | DocType |
data consumable,real-world datasets,heterogeneous datasets,minimal effort,semantic approach,data integration,data content aggregation,semantic relatedness,datasets demand,urban data,relevant datasets,content syndication,rss,atom,microdata,rdfa,rdf | Data science,Data integration,Semantic similarity,Affiliate marketing,World Wide Web,Subject-matter expert,Computer science,Microdata (HTML),RSS,RDF,Web syndication | Conference |
Citations | PageRank | References |
6 | 0.57 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Lopez | 1 | 725 | 48.98 |
| Spyros Kotoulas | 2 | 590 | 46.46 |
| Marco Luca Sbodio | 3 | 223 | 20.52 |
| Raymond Lloyd | 4 | 15 | 1.82 |