Open AccessCurriculum Semantic Retrieval System based on Distant Supervision
Author(s) -
Qiwei Tian,
Shixing Zhou,
Yu Cheng,
Jianxia Chen,
Yi Gao,
Shuijing Zhang
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.111603
Subject(s) - computer science , relationship extraction , graph , knowledge graph , conceptual graph , artificial intelligence , curriculum , relation (database) , ontology , information retrieval , knowledge extraction , natural language processing , machine learning , information extraction , data mining , knowledge representation and reasoning , theoretical computer science , psychology , pedagogy , philosophy , epistemology
Knowledge Graph is a semantic network that reveals the relationship between entities, which construction is to describe various entities, concepts and their relationships in the real world. Since knowledge graph can effectively reveal the relationship between the different knowledge items, it has been widely utilized in the intelligent education. In particular, relation extraction is the critical part of knowledge graph and plays a very important role in the construction of knowledge graph. According to the different magnitude of data labeling, entity relationship extraction tasks of deep learning can be divided into two categories: supervised and distant supervised. Supervised learning approaches can extract effective entity relationships. However, these approaches rely on labeled data heavily resulting in the time-consuming and laborconsuming. The distant supervision approach is widely concerned by researchers because it can generate the entity relation extraction automatically. However, the development and application of the distant supervised approach has been seriously hindered due to the noises, lack of information and disequilibrium in the relation extraction tasks. Inspired by the above analysis, the paper proposes a novel curriculum points relationship extraction model based on the distant supervision. In particular, firstly the research of the distant supervised relationship extraction model based on the sentence bag attention mechanism to extract the relationship of curriculum points. Secondly, the research of knowledge graph construction based on the knowledge ontology. Thirdly, the development of curriculum semantic retrieval platform based on Web. Compared with the existing advanced models, the AUC of this system is increased by 14.2%; At the same time, taking "big data processing" course in computer field as an example, the relationship extraction result with F1 value of 88.1% is realized. The experimental results show that the proposed model provides an effective solution for the development and application of knowledge graph in the field of intelligent education.