论文标题
知识：知识生成和链接文本
KnowGL: Knowledge Generation and Linking from Text
论文作者
论文摘要
我们提出了KNOWGL，该工具允许将文本转换为结构化的关系数据，该数据表示为一组具有给定知识图（KG）的Tbox（例如Wikidata）的Abox断言。我们通过利用预训练的序列到序列语言模型，例如巴特。鉴于句子，我们微调了这样的模型，以检测成对的实体提到并共同产生一组事实，这些事实由kg的完整语义注释组成，例如实体标签，实体类型及其关系。为了展示我们工具的功能，我们构建了一个由一组UI小部件组成的Web应用程序，可帮助用户导航浏览从给定输入文本中提取的语义数据。我们在https://huggingface.co/ibm/knowgl-large上提供知识模型。
We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART. Given a sentence, we fine-tune such models to detect pairs of entity mentions and jointly generate a set of facts consisting of the full set of semantic annotations for a KG, such as entity labels, entity types, and their relationships. To showcase the capabilities of our tool, we build a web application consisting of a set of UI widgets that help users to navigate through the semantic data extracted from a given input text. We make the KnowGL model available at https://huggingface.co/ibm/knowgl-large.
