多任务学习和改进的TextRank用于知识图表的完成
Hao Tian1,2, Xiaoxiong Zhang2, Yuhan Wang3
1School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一个新的知识图完成模型,该模型有效地使用实体描述和关系特征. 麻省理工学院-KGC模型显著提高了知识图完成任务的准确性.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 信息检索 信息检索
背景情况:
- 知识图完成 (KGC) 对于提高知识图和数据质量至关重要.
- 现有的KGC方法经常忽视关系特征,并与冗长的实体描述作斗争.
研究的目的:
- 提出一种新的多任务学习和改进的TextRank知识图表完成 (MIT-KGC) 模型.
- 通过有效利用实体描述和关系特征来解决当前KGC方法的局限性.
主要方法:
- 从实体描述中提取的关键上下文,使用改进的TextRank算法.
- 来自变压器 (ALBERT) 模型的简单双向编码器表示,用于作为文本编码器来减少参数.
- 多任务学习通过整合实体和关系特征来微调模型.
主要成果:
- 在WN18RR,FB15k-237和DBpedia50k数据集中,平均等级 (MR),Hit@10和Hit@3的显著改善.
- 与传统KGC方法相比,性能提升,证明了该模型的有效性.
- 具体的指标改进包括38%的MR增强WN18RR和1.5%的Hit@1改进DBpedia50k.
结论:
- 拟议的MIT-KGC模型有效地解决了知识图表完成方面的挑战.
- 改进的TextRank和ALBERT与多任务学习的整合产生了卓越的结果.
- 该模型显示了提高知识图质量和数据补充的巨大潜力.
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