# Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts
## 2 Related Work
### Knowledge-Based and Ontology-Based Approaches  
The system, which is based on a knowledge base, consists of information and data structured in an organized way. Question answering over a developed knowledge base based on the domain knowledge helps retrieve the information as demanded through a query. It is necessary to develop a dynamic knowledge base that can capture knowledge and dynamically update the knowledge base in such a case. Knowledge-based approaches are also used for learning and extracting knowledge from texts based on a specific domain. In [1], a method has been proposed for the development of a knowledge base based on the knowledge and behavior of operators in terms of a severe accident in nuclear power plant. The knowledge base was developed using 281 scientific publications, which were summarized and then the knowledge was extracted from them. The publications were related to the terms \"severe accident\" and \"nuclear\". The knowledge base that constitutes the knowledge graph consists of nodes and edges representing the causal relationships, entities, states, and affiliations. A knowledge graph was generated from each publication summary, and all these knowledge graphs were merged to constitute one main knowledge graph. From this methodology, it is observed that texts are skipped because it is considered repetitive or irrelevant to the topic of interest. This skipping of text is unacceptable in dealing with iTexts as each and every information in each of the instructions is required to be extracted and structured correctly in the knowledge base. Moreover, procedural knowledge structuring, information tracking, and sequencing are a significant part of structuring knowledge from iTexts which cannot be solved only with entity relationships. In [2], an agricultural knowledge base or framework has been proposed which helps to identify pests and diseases that affect a crop. An automatic ontology population tool has been developed. It helps extract relevant data from unstructured documents with the help of natural language processing techniques and update ontology. Their approach included representing information related to symptoms of plant diseases based on plant parts and damages. The proposed methodology was built into a system that could recognize crop pests with the help of the knowledge base. A system called \"Smart Farming\" was proposed in [3] for precision farming management, where a knowledge base was developed. Information was organized in the knowledge base in the form of a semantic network consisting of concepts and relations. The knowledge represented in the knowledge base was about crop production, production resources, agricultural machinery, equipment,and other resources. Ontological principles were adopted to design the domain model based on concepts, attributes, and interrelations. In [4], a knowledge-based strategy has been proposed for data management and mining in machining and to support decision making. The knowledge base consisted of manufacturing knowledge and a multi-level model that helped acquire knowledge and decision-making from the information stored in the knowledge base. Operation optimization knowledge base system (OOKBS) was designed in [5] for the operation optimization of a polyethylene process. Knowledge was represented using an ontology. Knowledge of polyethylene process, equipment operation, and operation optimization were integrated into the knowledge base. A neural network model was developed to identify the relationship between operating conditions and molecular weight distribution (MWD) parameters. In [6], an ontology-based approach was proposed, which classifies security requirements automatically. The security requirements were described with the help of 35 defined linguistic rules and 140 defined security keywords. The security requirements ontology was defined using description logic (DL).