# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 2 Related Work
OWL-Path was evaluated under different domains such as e-finance and e-tourism. Along the same lines, ONLI (Ontology-based Natural Language Interface) [26] allows non-expert users to query DBPedia with natural language, inferring the answer type expected by the user through an established question's classification. This system makes use of ontologies in order to represent both the syntactic question's structure and the question's context. Historically, agriculture has been benefitted from technology adoption. In the last few years, there have been numerous research efforts to provide ontology-based technological solutions for insect pest control and management. For instance, in [27], an ontology-based prototype for planning intercropping is presented. This system uses an optimization model that takes into account the farmer's constraint factors to illustrate maximum income as well as to minimize the cost of plant cultivation. In [28], the authors presented an ontology-based expert system for managing pests and diseases that affect grape crops from India. This system generates a knowledge base about grape pests and diseases from Web data sources and implements fuzzy logic rules that consider weather conditions for forecasting probable pests and diseases. SePeRe (Semantic Pest Recognition) [29] is a system for the early detection of plant diseases and pests which relies on a relational database that contains information about diseases and pests of Mediterranean crops such as olives, grapes, almonds, among others. This system receives as input an image of the crop and provides information about the disease affecting the plant. Most of the works previously analyzed present ontology-based decision support systems focused on the medical domain or, to a lesser extent, other domains such as manufacturing, environment, supply chain, and engineering vehicles. Specifically, on the identified systems focused on the agriculture domain, we have found that, none of them consider the diagnosis of crop insect pests. Table 1 presents a comparison of the most relevant works previously discussed. As can be seen from Table 1, ontologies have been implemented in decision support systems in different domains. However, relatively little work has been done in the development of decision support systems for insect pest diagnosis. For instance, works such as that presented in [28] are focused on only one crop, in this case, the grape. On the other hand, despite all works analyzed make use of ontologies as the main mechanism to represent expert's knowledge, they differ in the techniques used for developing the rule-based inference engine. Research efforts such as the presented in [17; 19; 21; 27] implemented rule-based inference engines that rely on Jena, a Java-based application framework for developing semantic web applications. Jena provides development tools and predefined reasoners including an RDFS (Resource Description Framework Schema) reasoner, and OWL-Lite reasoner, and a generic rule reasoner that supports user-defined rules written in the Jena rules language. Despite all these advantages, Jena provides a lower degree of expressiveness of the rule language in comparison with rule language such as SWRL [30]. Specifically, Jena rule language does not provide support for conjunction, disjunction and negation operations as well as universal and existential logical quantifiers. This fact limits the expressiveness that can be required to define complex rules that help to provide support in decision tasks from different domains. Meanwhile, for rule definition and inference purposes, in [16], the authors used Drools, an open-source software that provides a core business rules engine (BRE), a web authoring and rules management application (Drools Workbench). Despite Drools  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Objective** & **Domain** & \begin{tabular}{c} **Techniques Used for** \\ **Rule-Based Engine** \\ \end{tabular}