# An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum
## 6 Conclusions  
In general, a Knowledge Base (KB) can be seen as a technology that captures and stores knowledge from human experts. A Knowledge-Based System (KBS) uses a KB to make intelligent decisions, for example, to support decision-making processes, learning, and other activities. In most cases, a KB is highly domain-oriented, based on the expertise of experts in a specific domain. It is necessary to have sufficient knowledge about the field to be able to consume a KB properly. A challenge occurs when knowledge-sharing from a KB with persons who are not familiar with the domain, for example, citizen scientists. The involvement of citizen scientists has been recognized as an essential factor in science explorations. Their involvement is in collecting data and at the same time in consuming data, for example, to identify species. An approach for knowledge-sharing is through an ontology that can be seen as a shared common understanding about a domain. Using a common ontology for a variety of knowledge-sharing activities would guarantee consistency. In the domain of plant science, several ontologies have been developed to support knowledge-sharing among scientists. They were intended to be consumed by scientists who have knowledge about the domain but not by users who have less or even no knowledge about it. Figure 5: Degree predictability for combination of ranking mechanisms and for each query. In this paper, we tackled the challenge using an ontology-driven knowledge-sharing approach that is consumable by non-expert users. Our work started by eliciting knowledge from experts and formulated them in an ontology. The ontology was developed by focusing on generic characteristics and left out detailed information. Furthermore, we developed a faceted search application to consume the ontology and to provide relevant facets that users can use to refine their queries further. In this way, non-expert users should explore a KB about Capsicum with minimal background knowledge. We characterized users into four groups based on their ways to describe objects in a KB, in our case, parts of a plant. As a result, we introduced four matching mechanisms to identify relevant entities in our ontology and three ranking mechanisms to order the identified entities. To evaluate our search system, we measured the degree of predictability of produced facets. Given an identical query, the system should provide an equal output. We found that different types of queries can be mapped into one of the available matching mechanisms. Furthermore, by combining all three ranking mechanisms, we obtained the highest degree of predictability of the list of facets. In the future, we expand our current faceted search application vertically by using more sophisticated ontology and horizontally by using ontology from different areas. Specifically, we use a color index to avoid ambiguity in defining the value for the entity \"Color\". In its current implementation, we use pre-defined values such as \"Dark Green\" where every person might have a different definition. Additionally, we standardize values for specific entities. Values for the entity \"Shape\" such as \"rounded\" and \"bell shape\", should be standardized to avoid multiple interpretations. We are also considering transforming our ontology into a fuzzy ontology because fuzzy logic has suitable formalisms to handle imprecise and uncertain knowledge [51]. We envisioned that our approach has an essential impact on biodiversity science in general, where many KBs have been developed but they are difficult to be consumed by users outside of the domain. Further, non-expert users such as citizen scientists contribute significantly to knowledge-sharing and dissemination of science to the public. Conceptualization: Z.A. and D.S.R. ; data curation: H.F.M., L.P.M. and A.I. ; investigation: H.F.M., A.D.F. and T.D. ; methodology: Z.A. and D.S.R. ; resources: A.D.F. and T.D. ; writing--original draft preparation: Z.A., H.F.M., D.S.R., L.P.M. and A.I. ; writing--review and editing: Z.A. and D.S.R.