# An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum
## 2 Related Work  
We align our work with two broad research areas: the development of ontology as a bridge to unify diverse terminologies in plant science and ontology-driven faceted search. This section describes several related works from each area and outlines our contribution at the end. Ontology has been recognized as a vital component for interoperability across knowledge-based systems [7]. Ontologies are fundamental for unifying diverse terminologies and are increasingly used by scientists in many fields, including the online web search engines [13]. An ontology-underpinned emergency response system for water pollution accidents has been proposed to meet the demand of the government and public users for sustainable monitoring and real-time emergency response [14]. An ontological model can also represent context, driven by events, in academic domains by integrating five modular contexts, namely Person, temporal (time), physical space (location), network, and academic events [15]. Furthermore, an ontology also can be used to integrate a variety of quality assessment methods into a unified model for assessing the quality of a website [16]. Over the past years, many structured vocabularies, databases, and information systems have been developed to allow scientists to exchange knowledge about plant traits [17]. Ontology-based solutions are widely used to represent knowledge in this domain, which includes a set of terms to describe the classes in the domain and the relationships among terms [18]. For example, a schema to represent data from multiple biodiversity information systems that are available on the Web was constructed to enable Linked Biodiversity Data [19]. The International Rice Information System (IRIS) was developed to handle rice functional genomics data diversity, including genomic sequence data, molecular genetic data, expression data, and proteomic information [20]. The BRENDA enzyme information system ([[https://brenda-enzymes.org/](https://brenda-enzymes.org/)]([https://brenda-enzymes.org/](https://brenda-enzymes.org/)) (accessed on 28 June 2021)) was developed from a database to a competence center for enzyme-related information, which combines manually curated enzyme data with proteomic and genomic information [21]. The Pepper Expressed Sequence Tags (EST) database was constructed that consists of 122,582 sequenced ESTs and 116,412 refined ESTs that are available from 21 pepper EST libraries [22]. Much work has also been conducted utilizing local resources. For example, the development of an ontology for an Indonesian medicinal plant [23], an ontology for Thai Zingiberaceae [24], and an ontology for plant genetic resources in the Gene bank of the Institute of Plant Genetic Resources in the town of Sadovo [25]. Specifically for the Plant Ontology (PO), it can be used to describe not only plant anatomy and morphology but also the stages of plant development [8]. Knowledge and information detail about plant traits, genotype, and phenotype are usually used as basic information in PO. It should facilitate a formal description of phenotypes and standardized annotation of plant traits that accurately describe plant anatomy and morphology. In the previous release of PO, the so-called Plant Structure Ontology (PSO), there were three types of parent-child relationships in PSO that can be used to associate two terms, namely is_a, part_of, and develops_from [26]. PO in combination with another ontology can also be used to extract entity-quality relationships from digitized taxon descriptions [27]. On the other hand, faceted search as a human-computer interaction technique has been identified as a practical approach to handle a vast amount of data [28]. It provides interactivity and easy-to-use data visualization solution to guide the decision-making process through the classification of information, so-called \"facets\".