Name
Abstract | ||
|---|---|---|
Recent years have seen the rise of Web data, in particular Linked Data, with, up to now, more than 1000 datasets in the Linked Open Data Cloud (LOD). These datasets are mostly of entity-centric nature and are highly heterogeneous in terms of domains, language, schema, etc. Hence, the vision of uniformly querying such resources in the LOD has a long way to go. While equivalent entity instances across datasets are often linked by sameAs links, relations from different datasets and schemas are usually not aligned. In this paper, we propose an on-line instance-based relation alignment approach. The alignment may be performed during query execution and requires partial information from the datasets. We align relations to a target dataset using association rule mining approaches. We sample for equivalent entity instances with two main sampling strategies. Preliminary experiments, show that we are able to align relations with high accuracy, even if accessing the entire datasets is impossible or impractical. |
Year | Venue | Field |
|---|---|---|
2016 | EDBT | Data mining,Information retrieval,Computer science,On the fly,Linked data,Association rule learning,Sampling (statistics),Schema (psychology),Database,Cloud computing |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koutraki Maria | 1 | 11 | 6.03 |
| Nicoleta Preda | 2 | 173 | 14.40 |
| Dan Vodislav | 3 | 6 | 2.54 |