# An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum
## 2 Related Work
It has been used in many domains, for example, to provide dynamic faceted search solutions over enterprise databases for a domain-independent system [29] and to ensure a high-quality recommendation of scientific articles based on a query paper [30]. A visual recommendation system can also be built based on a faceted browsing technique that provides interactive navigation of automatically generated visualizations [31]. In the bioenergy domain, a faceted search system can be used to eliminate sense disambiguation of search terms [32]. A faceted search visualization technique can also support categorized access to heterogeneous and unstructured biomedical data sources [33]. This technique can also support a metasearch system to search and gather data resources from multiple Open Linked Data Projects [34]. Furthermore, it facilitates a coarse-grained and fine-grained exploration of geographic maps through interactive widgets [35]. In most of the faceted-search solutions, ontology plays a significant role. An ontology can be integrated with faceted navigation to improve information retrieval results through a query expansion mechanism [36]. By representing entities of an ontology as the facets and ontological instances as facets values, a personalized search interface can be constructedby matching the facets and user profile [37]. An Unsupervised Ontology was extracted from heterogeneous and unstructured biomedical content to be aligned with multiple existing biomedical ontologies to enable data exploration from heterogeneous biomedical resources [33]. For ontology-driven solutions, multiple search techniques/algorithms have been used to explore relatively large search spaces, for example, using the Hill-Climbing algorithm to learn the Bayesian network of a mushrooms dataset to identify to most relevant content [28]. A random walk-based framework has been used to induce a sub-network consisting of related nodes of the scientific article citations or content similarity network [30]. Relevant facets can also be predicted by using a Random Forest (RF) model [38], based on the most frequent queries of the most similar users [34], based on the ability of the user to provide desired values for each facet [29], as well as based on statistical and perceptual measures [31]. As mentioned in Section 1, several ontologies have been developed for plant research. The most prominent one is the Plant Ontology (PO), which adopts a data model of Gene Ontology (GO) that covers flowering plants in general. GO, for example, has been used to cluster and assemble the Pepper EST database to contribute to the analysis of gene synteny as part of a chili pepper sequencing project [22]. Using PO as the basis for an ontology-driven faceted search is an ideal solution. However, as mentioned in Section 1, the ontology is too complex and hardly understandable by non-expert users. In this work, we attempt to construct a search-based method that can be used to explore a knowledge base of Capsicum. The goal is to provide a faceted search that enables non-expert users to explore knowledge bases with less prior knowledge about characteristics of Capsicum. Our work contributes in at least two aspects:  
1. Development of an ontology that intends to communicate knowledge to non-experts users. Instead of using existing ontologies, we developed a small yet powerful ontology to describe the characteristics of Capsicum. The ontology was not intended to be complete but to be easily consumed by non-expert users. 2. Utilization of faceted search technique to drive search process. This technique has been widely used to overcome information overload or searching from a large amount of data. In contrast, our faceted search was intended to search from an unfamiliar database where the amount of data is not necessarily significant.