Mapping WordNets Using Structural Information

We present a robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select --among a set of candidates-- the node in a target taxonomy that bests matches each node in a source taxonomy. In particular, we use it to map the nominal part of WordNet 1.5 onto WordNet 1.6, with a very high precision and a very low remaining ambiguity.

Mapping Multilingual Hierarchies Using Relaxation Labeling

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from pre-existing lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select --among all the candidate translations proposed by a bilingual dictionary-- the right English WordNet synset for each sense in a taxonomy automatically derived from a Spanish monolingual dictionary. Although on average, there are 15 possible WordNet connections for each sense in the taxonomy, the method achieves an accuracy over 80%. Finally, we also propose several ways in which this technique could be applied to enrich and improve existing lexical databases.