###### Abstract  
The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in IS becomes a hot topic in different practical domains, such as engineering, IS, computer sciences, etc. As such, ontology- and fuzzy-based IS are being developed. Consequently, there is a need to provide a comprehensive systematic mapping study (SMS) to build a structure on the ontology- and fuzzy-based IS field of interest and to grasp the main ideas. This paper presents findings of SMS, based on the papers extracted from Web of Science and Scopus and employing a bibliometric analysis tool to automate keyword mapping. We conclude this paper by summarizing the previous work and identifying possible research trends, which future investigations can extend. The main finding indicates that ontology and fuzzy logic contribute to ISs by expanding traditional IS to be intelligent IS, which is applicable for solving complex, fuzzy, and semantically rich (ontological) information collection, saving, processing, sharing, and reasoning in different application domains according to users' needs in various countries. ontology; fuzzy logic; information system; ontology-based information system; fuzzy information system; fuzzy  
expressed formally and described in a particular formal language. Formal ontology, defined by a particular language, such as Ontology Web Language (OWL) or other, provides more formal semantics and reasoning support to IS [2]. The formal ontology definition for its automatic transformation to conceptual model and domain rules was proposed by [9; 10]. The general formalization of ontology and its role in the IS life cycle is analyzed in [12]. Additionally, as the authors of [13] state, a principled methodology for building ontologies is still lacking. Their taxonomic structure is often poor and confusing. Therefore, the authors suggested a better understanding of the properties corresponding to taxonomic nodes by proposing some meta-properties. In 1965, [PERSON] [14] proposed expressing uncertain concepts using fuzzy set theory. Since then, it has been applied in various fields, such as approximate reasoning, fuzzy control systems, pattern recognition, decision making, operation research, etc. [15]. As described in [16], most applications of fuzzy sets, such as uncertainty modeling [17; 18] and measuring [19], similarity measuring [20], reasoning with uncertainty [21], rule extraction [22; 23; 24], classification and feature selection [25; 26; 27] are related to IS. According to [16; 28]: _\"If each of its attributes in an information system is fuzzy, then this information system is called a full fuzzy information system\"_. The authors of [29] defined fuzzy IS as having objects with fuzzy relations among them. A fuzzy IS may be regarded as an IS under a fuzzy environment [30]. A fully fuzzy information system is an IS where each of its attributes determines a fuzzy set on the object set [31]. Consequently, in the literature, we can find different studies on either ontology-driven IS, such as [32; 33; 34; 35], or fuzzy IS, such as [16], where either ontologies or fuzzy sets enrich IS with semantic power in different ways and for various purposes. Thus far, the study of both ontology- and fuzzy-based IS has been reported weakly. Here, special attention should be paid to fuzzy ontology IS, as they are close to our topic of interest. It is necessary to explain how the existing studies on the fuzzy ontology IS topic, such as [2; 36], differ from our study. The main difference between the mentioned studies on fuzzy ontology IS is that they deal with fuzzy extensions to ontology in IS. In the present study, we look more broadly; that is, this study includes both fuzzy ontology IS and IS, in which not only the ontology but also other attributes [16] are fuzzy.