Towards a Type-2 Fuzzy Description Logic for Semantic Search Engine

Ruixuan Li, Xiaolin Sun, Zhengding Lu, Kunmei Wen, Yuhua Li

Abstract: Classical description logics are limited in dealing with the crisp concepts and relationships, which makes it difficult to represent and process imprecise information in real applications. In this paper we present a type-2 fuzzy version of $\mathcal{ALC}$ and describe its syntax, semantics and reasoning algorithms, as well as the implementation of the logic with type-2 fuzzy OWL. Comparing with type-1 fuzzy $\mathcal{ALC}$ , system based on type-2 fuzzy $\mathcal{ALC}$ can define imprecise knowledge more exactly by using membership degree interval. To evaluate the ability of type-2 fuzzy $\mathcal{ALC}$ for handling vague information, we apply it to semantic search engine for building the fuzzy ontology and carry out the experiments through comparing with other search schemes. The experimental results show that the type-2 fuzzy $\mathcal{ALC}$ based system can increase the number of relevant hits and improve the precision of semantic search engine.