# Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts
## 2 Related Work
### Entity-Relation Extraction  
The main objective of the entity recognition and relation extraction task is to determine the relational structure of the mentioned entities from unstructured texts. The task has two subtasks, namely (i) named entity recognition (NER) [10] and (ii) relation extraction (RE) [11]. These tasks help to connect each entity with other ones and are very useful for developing a semantic network with nodes and edges. In this research work, our knowledge base help to identify the entity and key phrases from iTexts and represent the relationship among them based on different operation in the form of HESN. In [12], an approach has been proposed for constructing knowledge graphs with the help of a task named relation extraction and linking. Their approach is dependent on information extraction (IE) tasks for obtaining the named entities and relations. Finally, these are linked using data and standards of the semantic web. Initially, the input text is transformed into resource description framework (RDF) triples using the combination of natural language processing and information extraction operation. The information extraction operation includes tasks like document acquisition and preprocessing the input text, extracting named entities and their association with the grammatical unit, semantic relation extraction using the OpenIE approach and associating it with semantic information provided by an approach termed as semantic role labeling (SRL) that helps to identify the order and selection of elements which are to be finally represented through RDF triples. In total, 605 IT news webpages were downloaded and used for the evaluation of their research methodology. It had about 12,015 sentences which were processed to construct the RDF statements. RDF triples or statements are dependent upon the subject, object, and predicate of a sentence. Many sentences were ignored that had relations containing no named entities in subject and object. As a result, RDF statements for such sentences were not createdand, thus, ignored. In [13], a relation extraction method for construction of COVID-19 information knowledge graph based on deep learning was proposed. It is another open information extraction (OpenIE) system based on unsupervised learning without any pre-defined dataset, although a COVID-19 entity dictionary was created and used for scraping related information. The proposed method extracts knowledge from documents consisting of information related to COVID-19 and constructed a knowledge base that consisted of connecting words between COVID-19 entities, which was captured from COVID-19 sentences. The proposed model could identify a relation between COVID-19-related entities using (BERT), and it does not need any pre-built training dataset. In [14], researchers presented a neural model to extract entities and their relation from texts. The basic layers of their proposed model consisted of embedding layer, bidirectional sequential long short term memory (BiLSTM) layer, conditional random fields (CRF) layer, and sigmoid layer. The conditional random fields (CRF) layer was used to recognize entities and the sigmoid layer for entity relation extraction. The task was modeled as a multi-head selection problem where an entity may have multiple relations in a text. The model does not rely on hand-crafted or external natural language processing tools, such as parts-of-speech (POS) tagger, dependency parsers, etc. Extracting semantic relation from text has been performed by a group of researchers in [15]. Two models named Rel-TNG and Type-TNG were proposed that used topic n-Grams (TNG). These two models were able to show similar performance measure for Rel-LDA and Type-LDA, but the models outperformed Rel-LDA and Type-LDA when there was prior knowledge available. GENIA and EPI datasets were used for this experiment which are biomedical texts. Two types of relationships were annotated: PROTEIN-COMPONENT and SUBUNIT-COMPLEX.