Name
Abstract | ||
|---|---|---|
Co-references are traditionally used when integrating data from different datasets. This approach has various benefits such as fault tolerance, ease of integration and traceability of provenance; however, it often results in the problem of entity consolidation, i.e., of objectively stating whether all the co-references do really refer to the same entity; and, when this is the case, whether they all convey the same intended meaning. Relying on the sole presence of a single equivalence (owl:sameAs) statement is often problematic and sometimes may even cause serious troubles. It has been observed that to indicate the likelihood of an equivalence one could use a numerically weighted measure, but the real hard questions of where precisely will these values come from arises. To answer this question we propose a methodology based on a graph clustering algorithm. |
Year | Venue | Keywords |
|---|---|---|
2013 | Lecture Notes in Computer Science | Equivalence Mining,Co-references,Linked Data |
Field | DocType | Volume |
Computer science,Linked data,Equivalence (measure theory),Fault tolerance,Natural language processing,Artificial intelligence,Clustering coefficient,Traceability,Distributed computing | Conference | 8186 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Bartolomeo | 1 | 14 | 5.28 |
| Stefano Salsano | 2 | 799 | 78.03 |
| Hugh Glaser | 3 | 92 | 5.47 |