## 4 Discussion  
Ontology and fuzzy logic are beneficial in IS, since they extend traditional IS to be intelligent IS by its semantic enrichment, sharing and reusing knowledge on the domain. Therefore, it is important to understand the structure of ontology- and fuzzy-based IS field of interest and to grasp the main ideas and results of those systems. Finally, in this section, we can answer the RQs and discuss the obtained results of SMS. In this research, we have applied a systematic mapping with bibliometric analysis techniques to answer the main research question (MRQ): _How do ontology and fuzzy logic contribute to information systems?_ Consequently, MRQ was decomposed to the following sub-questions: (RQ-1) _When is ontology- and fuzzy-based IS research published?_, (RQ-2) _Which ontology- and fuzzy-based IS development topics are covered?_, (RQ-3) _What are the main future directions found in the analyzed topic?_, and (RQ-4) _What are the visible trends for the countries participating in the study of ontology- and fuzzy-based IS?_  
The conducted systematic mapping study (SMS) on the ontology- and fuzzy-based IS topic shows a significant increase of papers since 2003 (RQ-1). It can be attributed to technological development and raise the applicability of fuzzy theory to present uncertainties in various application domains. Moreover, our observed growth of publications on the ontology- and fuzzy-based IS topic corresponds to the growth of publications on the ontology topic found by [53]. To answer RQ-2, we have performed a keywords occurrence, co-occurrence, and clustering analysis using VOSviewer. Moreover, the developed keywords occurrence maps were analyzed applying the Pareto distribution. The most occurring keywords map (Pareto distribution--20) allows us to determine the most analyzed ontology- and fuzzy-based IS topics. They are the following: fuzzy ontology information systems, semantic web, and reasoning. The cluster analysis of the most occurring keywords map allows us to identify three main areas in ontology- and fuzzy-based IS as the following: (1) users' needs, (2) knowledge representation through fuzzy ontology for reasoning about concepts, and (3) data framework development according to context and rules, i.e., IS development. The co-occurrence matrix confirms these findings. The moderately occurring keywords map (Pareto distribution--80) allows us to determine the sub-topics (RQ-2) of the main identified topics (Pareto distribution--20) and to view the possible future directions (RQ-3). Based on keywords occurrence and clustering analysis, we have found different application domains of ontology- and fuzzy-based IS (i.e., image recognition, scenario analysis, user's experience collection, decision making, information retrieval, semantic similarity, and data mining) and fuzzy information representation and management topic. Moreover, depending on the application domain, in modern IS, we observe a need for collecting, saving, sharing, and analyzing data in different forms, such as documents, images, scenarios, sensors data, user profiles data, etc. Information retrieval is also observed through the collection, access, interpretation, decision making, monitoring, inference, etc. Knowledge is found through knowledge representation, knowledge management, rules, axioms, fuzzy knowledge, uncertain knowledge, etc. The co-occurrence matrix confirms that semantically rich information retrieval, storage, and analysis requires traditional IS to be extended to semantically rich and intelligent IS. The moderately occurring keywords according to APY allow us to predict possible future directions (RQ-3) in the ontology- and fuzzy-based IS topic as the following: social network, social medium, IoT (Internet of Things), sentiment analysis, web ontology language, cloud service, sensor, and recommendation system. We have analyzed two separate country maps for WoS and Scopus to investigate visible trends for the countries participating in the study of ontology- and fuzzy-based IS field (RQ-4).