CDE: A Concept-Driven Joint Extraction Method for Computer Science Textbooks
Source abstract
Abstract
Addressing the challenges of dense conceptual content and intricate knowledge relations in computer science textbooks, where traditional pipeline-based information extraction suffers from error propagation and semantic decoupling, this paper proposes a concept-driven joint extraction method termed CDE (Concept-Driven Extraction).First, the model’s ability to focus on domain-specific terminology is enhanced through conceptual priors and attention re-weighting. This is integrated with a predefined schema and structured instruction templates to achieve normalized output for both entities and relations. Second, efficient domain knowledge transfer for computer science textbooks is realized by performing Low-Rank Adaptation (LoRA) fine-tuning on the Qwen3-4B large language model. Finally, the construction of the computer science textbook knowledge graph is accomplished using the Neo4j graph database. On a self-constructed instruction dataset of computer science textbooks, CDE achieves an F1 score of 81.83%, representing an improvement of approximately 2.47 percentage points over the LKD-KGC baseline. This performance significantly surpasses that of traditional pipeline models and existing joint extraction approaches. Experimental results demonstrate that CDE can effectively improve knowledge extraction accuracy in the textbook domain, thereby providing a novel research avenue for the rapid construction of knowledge graphs for computer science educational materials.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Text Analysis Techniques · Advanced Graph Neural Networks · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China
Xinjiang Engineering Technology Research Center for Smart Education and Application, Urumqi 830054, China
Key Laboratory of Aerospace Information Security and Trusted Computing, School of Cyber Science and Engineering, Wuhan University, Wuhan 430000, China
School of Journalism and Communication, Xinjiang University, Urumqi 830046, China
Funding
Xinjiang Uygur Autonomous Region Innovation Environment (Talent and Base) Construction Special Program, Natural Science Program · Xinjiang Normal University · National Natural Science Foundation of China · National Social Science Foundation of China
References (25)
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