Ontolearn可批量学习知识图谱中的类表达式并转为自然语言。
Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
- 基于符号与神经符号方法，自动从知识图谱中学习类表达式。
- 支持将复杂表达式转换为SPARQL查询，可远程操作知识库。
- 集成大模型实现类表达式到自然语言的自动描述生成。
本文提出Ontolearn——一个用于在大规模知识图谱上学习OWL类表达式的框架。该框架集成了EvoLearner和DRILL等前沿符号与神经符号类表达式学习算法，可对知识图谱中的实例进行分类。Ontolearn还内置基于LLM的表述模块，能将复杂的OWL类表达式转化为自然语言句子；通过将类表达式映射为对应SPARQL查询，支持远程三元组存储的操作。代码已开源，地址为https://github.com/dice-group/Ontolearn。
原文摘要 · Abstract (English)
In this paper, we present Ontolearn-a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sentences. By mapping OWL class expressions into respective SPARQL queries, Ontolearn can be easily used to operate over a remote triplestore. The source code of Ontolearn is available at https://github.com/dice-group/Ontolearn.
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