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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Research on Domain-Specific Knowledge Graph Based on the RoBERTa-wwm-ext Pretraining Model.
Xingli Liu1, Wei Zhao1, Haiqun Ma2
1School of Computer Science and Technology, Heilongjiang University of Science and Technology, Harbin 150020, Heilongjiang, China.
Computational Intelligence and Neuroscience
|October 24, 2022
Summary
This study introduces a deep learning approach for domain-specific knowledge graph construction, enhancing information extraction and fusion. The method effectively builds knowledge graphs from open-source intelligence, improving accuracy and efficiency.
Area of Science:
- Artificial Intelligence
- Natural Language Processing
- Knowledge Representation
Background:
- Effective domain-specific knowledge graph construction remains a challenge.
- Integrating information extraction and knowledge fusion is crucial for building comprehensive knowledge graphs.
Purpose of the Study:
- To develop an effective deep learning algorithm for domain-specific knowledge graph construction.
- To extract entities and relationships from open-source intelligence and fuse them into a knowledge graph.
Main Methods:
- Utilized RoBERTa-wwm-ext pretraining model for named entity recognition and relationship extraction.
- Implemented a knowledge fusion framework with longest common attribute entity alignment.
- Employed text similarity and classification algorithms for verification.
Main Results:
- RoBERTa-wwm-ext achieved the best results in named entity recognition (F1 value up to 83.07%).
- RoBERTa-wwm-ext relationship extraction model showed significant improvement (20%-30%) over recurrent neural networks.
- Entity alignment algorithm based on longest common subsequence attribute similarity performed optimally.
Conclusions:
- The proposed deep learning approach provides an effective method for domain-specific knowledge graph construction.
- This research lays the groundwork for future applications like domain-specific intelligent Q&A systems.
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