论文标题
FABKG：使用结构化和非常规的非结构化知识来源的制造科学领域的知识图
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source
论文作者
论文摘要
随着对大规模信息处理的需求的增长，基于知识图的方法已经获得了代表一般和领域知识的突出性。这种一般表示的发展至关重要，尤其是在诸如智能过程和适应性教育可以增强的制造领域。尽管文本在这些领域中持续积累，但缺乏结构化数据仍创造了信息提取和知识传递障碍。在本文中，我们报告了基于实体和商业和教育用途的实体和关系数据开发坚固知识图的工作。为了创建Fabkg（制造知识图），我们使用了教科书索引单词，研究纸关键字，Fabner（Manufacturing Ner）来提取Wikidata中包含的子知识基础。此外，我们通过利用学生笔记提出了一种新颖的众包来创建KG的方法，该方法包含宝贵的信息，但并未被捕获为有意义的信息，不包括他们在个人准备和书面考试中的使用中的用途。我们创建了一个使用所有数据源的知识图，该图形包含65000+的三元组。我们还显示了针对教育目的的特定领域的问题回答和基于表达/公式的问题的用例。
As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continuous accumulation of text in these domains, the lack of structured data has created information extraction and knowledge transfer barriers. In this paper, we report on work towards developing robust knowledge graphs based upon entity and relation data for both commercial and educational uses. To create the FabKG (Manufacturing knowledge graph), we have utilized textbook index words, research paper keywords, FabNER (manufacturing NER), to extract a sub knowledge base contained within Wikidata. Moreover, we propose a novel crowdsourcing method for KG creation by leveraging student notes, which contain invaluable information but are not captured as meaningful information, excluding their use in personal preparation for learning and written exams. We have created a knowledge graph containing 65000+ triples using all data sources. We have also shown the use case of domain-specific question answering and expression/formula-based question answering for educational purposes.
