API comparison knowledge extraction via prompt-tuned language model

Yang, Yangrui; Zhu, Yunlong; Chen, Sisi; Jian, Pengpeng

doi:10.1016/j.cola.2023.101200

Cited by 4 publications

(1 citation statement)

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“…Entity relationship extraction is an information extraction method based on natural language processing and machine learning techniques, which can identify and extract entities and relationships from unstructured or semi-structured texts and represent them as structured data in the form of triplets and knowledge graphs. Existing extraction methods based on summary sentence types [3][4][5] or sequence annotations [6][7][8] are often accompanied by great labor overhead, and to alleviate this labor overhead, This article describes the use of the APICKnow [9] to define an Education Entity Relationship Joint Extractor(EERJE) for extracting knowledge triplets. Using the graph database Neo4j as the knowledge storage carrier, the triplet knowledge is presented in the form of a knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

Knowledge graph empowerment from knowledge learning to graduation requirements achievement

Yang,

Chen,

Zhu

et al. 2023

PLoS ONE

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A deep understanding of the relationship between the knowledge acquired and the graduation requirements is essential for students to precisely meet the graduation requirements and to become human resources with specific knowledge, skills and professionalism. In this paper, we define the ontology layer of the knowledge graph by deeply analyzing the relationship between graduation requirement, course and knowledge. Based on the implementation of the concept of Outcome Based Education, we use Knowledge extraction, fusion, reasoning techniques to construct a hierarchical knowledge graph with the main line of "knowledge-course-graduation requirements. In the process of knowledge extraction, in order to alleviate the huge labor overhead brought by traditional extraction methods, this paper adopts a transfer learning method to extract triadic knowledge using the multi-task framework EERJE, Finally, knowledge reasoning was also performed with the help of LLM to further expand the knowledge scope. The comprehensiveness, correctness and relatedness of the data were evaluated through the experiment, and the F1 value of the ternary group extraction was 87.76%, the accuracy rate of entity classification was 85.42%, the data coverage was more comprehensive, and the results showed that the data quality was better, and the knowledge graph constructed in this way can fully optimize the organization and management of teaching resources, help students intuitively and comprehensively grasp the correlation and difference between graduation requirements and various knowledge points, and let the Students can carry out personalized independent learning through the navigation mode of knowledge graph, strengthen their weak links, and complete the relevant graduation requirements, which effectively improves the degree of students’ graduation requirements achievement. This new paradigm of knowledge graph enabled teaching is of reference significance for engineering education majors to improve the degree of graduation requirements achievement.

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