# Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts
## 2 Related Work
### Limitations in Case of iText  
Different methods and approaches of information extraction and knowledge structuring discussed are suitable for learning from regular or standard texts or paragraphs consisting of information about different entities and their relationships. Relationships among entities were established in the form of RDF triples, semantic networks or knowledge graph. Knowledge extraction is performed from texts and developed knowledge-base. Ontology is developed for better success of the knowledge extraction process. Semantic meaning is extracted with the help of ontology. Identification of different problems is performed based on established knowledge-base or ontology. Such approaches have limitations in capturing knowledge from iTexts that consists of a set of instructions related to how to conduct an operation or activity. The structure of the sentence in iText is a bit different from regular paragraphs. Firstly, in iTexts, there could be an entity having different values or relationships based on different operations. Hence, it is essential to keep track and structure knowledge of the values and relationships of an entity based on different operations. Secondly, there could be relationships between two entities and between an entity and other terms like \"move\", \"shift\", \"high\", \"low\", or any number. Traditional triplets extraction or RDF triples are extracted from sentence structure consisting of subject, object, and predicate. This method is not perfect as it sometimes consists of error or contradictory information [18]. Moreover, having more than 1 triple in a sentence is again required to be handled which is also an expensive process [19]. Predicting triple in a sentence is also another task that needs effort. It is also not always possible to get information in the form of triplets. In Figure 1, the iText \"Wear gloves\" consists of two words only. Here, a particular user could be considered as subject. However, in case of iText, this is not always applicable. Considering \"user\" as subject and relating this entity with other two terms will create confusion in the case where this \"user\" is already defined as any particular human role in the operation title. Therefore, if the operation title is \"Must perform tasks for Lab Operators\", then wearing gloves is instructed for lab operators instead of \"user\". Duplet based relation extraction helps to create relation between the action \"wear\" and entity \"gloves\". This piece of information also consists of tags from operation title that helps to know that wearing gloves is applicable for lab operators. This tracking of information is explained in more detail in methodology part of this paper. Moreover, ontology or domain knowledge in case of RDF needs to be enriched. However, in our case, the domain knowledge is developed in a simple way. It consists of different class names and words or phrases defined under each class name, which is good enough for generating duplet relations. Further explanation about domain knowledge is done in the methodology part of this paper. Another example can be drawn when numbers are considered. In case of duplets, our approach identifies number and can make relation with another term or phrase directly, whereas, it is a complicated task when triplets are considered. For example, \"Pump must have pressure 4 Pa\". From here, we get the duplets (pump, pressure), (4, pressure). Here, the value 4 is directly assigned with pressure which will be an helpful information for complex reasoning when this operational instruction is considered. This is not possible with triplets directly. For this reason, our research deals with duplets. The domain knowledge also needs to be well defined. This will help to identify different terms found in iTexts and structure knowledge accordingly. This paper proposes a knowledge base that captures knowledge from iTexts, represents the knowledge using HESN as part of the knowledge base, and dynamically updates the knowledge base.