Synset

This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a new model of WordNet that organizes the different senses of polysemy words as well as the single sense words based on the clue words. These clue words for each sense of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the polysemy word in the given context using knowledge based Word Sense Disambiguation (WSD) algorithms. The clue word can be a noun, verb, adjective or adverb.

Semantic relations are the cornerstone of any wordnet. In most wordnets created thus far, however, the nature of the arguments of such relations is unclear. The synset, usually considered the basic unit of a wordnet, relies on the notion of synonymy, distinctly difficult to define, and on the even more vague notion of a concept. We argue that the synset should be consistently replaced by the lexical unit as the fundamental unit of a wordnet. A wordnet is a lexical database that operates as a dictionary or a thesaurus. Users expect words in such a resource, and they can understand words even without the benefits of syntax. Semantic or lexico-semantic relations, then, link meanings bound to words, or more generally to lexical units. The practical effect of this work is the adoption of lexical units as the basic building blocks of a Polish WordNet namedplWordNet.

This paper presents a method of building a gold standard test set of Thai WordNet. The results of this research can be utilised for evaluating or comparing the results from different approaches of Thai WordNet construction. In this research, a part of Thai WordNet is carefully handcrafted from Common Base Concepts ’ FirstOrderEntities with five translation resources. However, we found that to build a gold standard test set is not easy as finding words that can fit to the definition of synsets; cultural gaps between the different languages have to be aware of.

Obtaining correct and relevant information at the right time to user’s query is quite a difficult task. This becomes even complex, if the query terms have many meanings and occur in different varieties of domain. This paper presents a fuzzy-ontology based information retrieval system that determine the semantic equivalence between terms in a query and terms in a document by relating the synonyms of query terms with those of document terms. Hence, documents could be retrieved based on the meaning of query terms. The challenge has been that surface form does not sufficiently retrieve relevant document to user’s query. However, the results presented showed that the Fuzzy-Ontology Information Retrieval system successfully retrieve relevant documents to user’s query. This is irrespective of different meaning and varieties of domain. The System was tested on words with different meanings and some set of user’s query from varied domains.