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. © 2013 Springer-Verlag.
CITATION STYLE
Bartolomeo, G., Salsano, S., & Glaser, H. (2013). On the likelihood of an equivalence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8186 LNCS, pp. 2–11). https://doi.org/10.1007/978-3-642-41033-8_2
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