Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of sparsity of the synonymy dictionaries.
Meyer and Gurevich @cite_1 presented an approach for construction of an ontologized version of Wiktionary, by formation of ontological concepts and relationships between them from the ambiguous input dictionary, yet their approach does not involve graph clustering.
@cite_1
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The semi-automatic development of ontologies is an important field of research, since existing ontologies often suffer from their small size, unaffordable construction cost, and limited quality of ontology learning systems. The main objective of this chapter is to introduce Wiktionary, which is a collaborative online dictionary encoding information about words, word senses, and relations between them, as a resource for ontology construction. The authors find that a Wiktionary-based ontology can exceed the size of, for example, OpenCyc and OntoWordNet. One particular advantage of Wiktionary is its multilingual nature, which allows the construction of ontologies for different languages. Additionally, its collaborative construction approach means that novel concepts and domain-specific knowledge are quick to appear in the dictionary. For constructing their ontology OntoWiktionary, the authors present a two-step approach that involves (1) harvesting structured knowledge from Wiktionary and (2) ontologizing this knowledge (i.e., the formation of ontological concepts and relationships from the harvested knowledge). They evaluate their approach based on human judgments and find their new ontology to be of overall good quality. To encourage further research in this field, the authors make the final OntoWiktionary publicly available and suggest integrating this novel resource with the linked data cloud as well as other existing ontology projects.