## 3 Results
### Keywords Occurrence Analysis (RQ-2 and RQ-3)
#### 3.2.2 The Moderately Occurring Keywords  
The moderately occurring keywords map is presented in Figure 4. For its development, we have restricted the number of keywords' occurrences from 5 to 17 (it corresponds to 80% of all found keywords by Pareto distribution). VOSviewer has identified 147 keywords that meet the threshold. The 10 most occurring keywords are the following: retrieval (55), collection (16), comparison (16), experience (16), imprecision (16), preference (16), quality (16), support (16), attribute (15), and case study (15).  
As can be seen from Figure 4, those keywords were divided into seven clusters. The closest and the most occurring keywords in each cluster are the following: (1) preference, attribute, image, recommendation system, and fuzzy markup language; (2) collection, experience, support, characteristic, fuzzy rule, identification, and scenario; (3) patient, decision making, activity, decision support system, location, monitoring, and practice; (4) imprecision, knowledge representation, knowledge management, interval, and fuzzy information; (5) retrieval, quality, search engine, information retrieval system, user query, and cost; (6) comparison, case study, dataset, keyword, and semantic similarity; and (7) data mining, formal concept analysis, entity, treatment, and www. Preliminary conclusions are that the 1 st, 2 nd, 3 rd, 5 th, 6 th, and 7 th clusters represent different application domains of ontology- and fuzzy-based IS. The 4 th cluster represents fuzzy information representation and management topic. Moreover, in each cluster, we can see data in different forms, such as documents, images, scenarios, sensors data, user profiles data, etc. Information retrieval is also observed through collection, access, interpretation, decision-making, monitoring, inference, etc. Knowledge is found through knowledge representation, knowledge management, rules, axioms, fuzzy knowledge, uncertain knowledge, etc.  
Figure 3: The most occurring keywords map.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Keywords** & **Feature** & **Framework** & \begin{tabular}{c} **Fuzzy** \\ **Extension** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy** \\ **Logic** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy** \\ **Onology** \\ \end{tabular} & **Fuzzy Set** & \begin{tabular}{c} **Fuzzy Set** \\ **Theory** \\ \end{tabular} & \
\begin{tabular}{c} **Information** \\ **Retieval** \\ \end{tabular} &
\begin{tabular}{c} **Knowledge** \\ **Base** \\ \end{tabular} & **User** \\ \hline accuracy & 6 &  
Table 3 presents the part of the co-occurrence matrix of the moderately occurring keywords in the ontology- and fuzzy-based IS field. As can be seen, the most co-occurred keywords are the following: keyword and retrieval (8), collection and retrieval (6), image and retrieval (6), fuzzy semantic retrieval and semantic query expansion (5), semantic retrieval and fuzzy semantic retrieval (5), and semantic query expansion and semantic retrieval (5). Consequently, it can be said that semantically rich information retrieval requires semantic query expansion.  
Figure 5 presents the moderately occurring keywords distribution according to Average Publication Year (APY), which indicates the average publication year of the documents in which a keyword occurs [57]. The yellow-colored keywords present currently the most analyzed topics in the ontology- and fuzzy-based IS field as the following: social network (APY 2019), social medium (APY 2018), IoT (Internet of Things) (APY 2018), sentiment analysis (APY 2017), web ontology language (APY 2017), cloud service (APY 2016), sensor (APY 2016), recommendation system (APY 2016), dataset (APY 2016), wordnet (APY 2016), SWRL (Semantic Web Rule Language) (APY 2015), etc.  
Figure 4: The moderately occurring keywords map.