FOGA: A Fuzzy Ontology Generation Framework for Scholarly Semantic Web Thanh Tho Quan 1 , Siu Cheung Hui 1 and Tru Hoang Cao 2 1 School of Computer Engineering, Nanyang Technological University, Singapore {PA0218164B, asschui}@ntu.edu.sg 2 Faculty of Information Technology, Hochiminh City University of Technology, Hochiminh City, Vietnam tru@dit.hcmut.edu.vn Abstract. This paper proposes the FOGA (Fuzzy Ontology Generation frAmework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: Fuzzy Formal Concept Analysis, Fuzzy Conceptual Clustering and Fuzzy Ontology Generation. First, Fuzzy Formal Concept Analysis incorporates fuzzy logic into Formal Concept Analysis (FCA) to form a fuzzy concept lattice. Fuzzy Conceptual Clustering then constructs the concept hierar- chy from the fuzzy concept lattice. Finally, Fuzzy Ontology Generation generates the fuzzy ontology from the concept hierarchy. In this paper, we will also discuss the application of the FOGA framework to gener- ate a scholarly ontology for the Scholarly Semantic Web from a citation database. The performance of the proposed FOGA framework is given based on the scholarly ontology generated. Keywords: Formal Concept Analysis, Fuzzy Logic, Conceptual Clustering, Ontol- ogy Generation 1 Introduction Ontology is a conceptualization of a domain into a human understandable, but machine-readable format consisting of entities, attributes, relationships and ax- ioms [1]. Ontology uses classes to represent concepts. Ontology also supports taxonomy and non-taxonomy relations between classes. However, the concep- tual formalism supported by typical ontology may not be sufficient to represent uncertainty information that is commonly found in many application domains. For example, keywords extracted from scientific publications can be used to in- fer the corresponding research areas, however, it is inappropriate to treat all keywords equally as some keywords may be more significant than others. In ad- dition, it is sometimes difficult to judge whether a document belongs completely to a research area or not. To tackle this type of problems, one possible solution is to incorporate fuzzy logic into ontology to handle uncertainty data. Traditionally, fuzzy ontology is