Methods and Tools to Advance the Retrieval of Mathematical Knowledge from Digital Libraries for Search-, Recommendation- and Assistance-Systems

促进从数字图书馆检索数学知识以用于搜索、推荐和辅助系统的方法和工具

• 批准号: 350192710
• 负责人: Professor Dr.-Ing. Bela Gipp
• 金额: --
• 依托单位: Niedersächsische Staats- und Universitätsbibliothek
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/350192710?language=en
• 关键词: Methods; Tools; Advance; Retrieval; Mathematical

The goal of our project is to investigate fundamental methods and tools for making mathematical knowledge accessible to information retrieval tools. Achieving this goal requires methods to reliably extract mathematical knowledge from documents. In the domain of natural language processing (NLP), a number of well-established, general purpose text processing methods and tools exist that are applied to a text to enable domain specific extraction tasks. Similar to state-of-the-art text processing tools, such as the Stanford NLP toolkit, our research will determine how similar tools for processing mathematical language can be realized.Our approach is to expand upon the concept of Mathematical Language Processing (MLP), a concept for which we have already demonstrated its feasibility when we presented it at the ACM SIGIR 2016. In the context of this project, we will expand upon our preliminary research to make the approach more effective and applicable for real world mathematical information retrieval applications. Specifically, the project has the following objectives:1. Identify mathematical formulae and expressions in documents, and reliably differentiate them from similar or neighboring structures.2. Perform type detection and tokenization of mathematical expressions.3. Extract the corresponding mathematical concepts from the tokenized mathematical formulae and expressions.Our goal is to enable other scientists to use our methods and tools for mathematical language processing to tackle their own novel problems. We hope that MLP will continue to improve in this process, as was once the case for early NLP approaches.A wide variety of applications would benefit from advancements to mathematical information retrieval. In the STEM disciplines, improvements could be made to academic literature search, literature recommendation, and even plagiarism prevention. Additionally, expert search or applications in pure mathematics, such as theorem search or definition lookup, would significantly benefit from our developments. Applications beyond STEM fields include the improvement of tutoring assistance tools, as well as patent search and enterprise search, which could become more valuable to companies if they integrate math-aware information retrieval methods.

我们的项目的目标是调查的基本方法和工具，使数学知识的信息检索工具。实现这一目标需要从文档中可靠地提取数学知识的方法。在自然语言处理（NLP）领域中，存在许多成熟的通用文本处理方法和工具，其应用于文本以实现领域特定的提取任务。类似于最先进的文本处理工具，如斯坦福大学NLP工具包，我们的研究将确定如何实现类似的数学语言处理工具。我们的方法是扩展数学语言处理（MLP）的概念，我们已经在ACM SIGIR 2016上展示了这一概念的可行性。在这个项目的背景下，我们将扩大我们的初步研究，使该方法更有效和适用于真实的世界的数学信息检索应用。具体而言，该项目有以下目标：1.识别文档中的数学公式和表达式，并可靠地将它们与相似或相邻的结构区分开来。2.对数学表达式进行类型检测和标记化.从标记化的数学公式和表达式中提取相应的数学概念。我们的目标是使其他科学家能够使用我们的数学语言处理方法和工具来解决他们自己的新问题。我们希望MLP在这个过程中继续改进，就像早期的NLP方法一样。各种各样的应用将从数学信息检索的进步中受益。在STEM学科中，可以改进学术文献搜索，文献推荐，甚至防止剽窃。此外，专家搜索或纯数学中的应用程序，如定理搜索或定义查找，将大大受益于我们的发展。STEM领域以外的应用包括改进辅导辅助工具，以及专利搜索和企业搜索，如果他们整合数学感知信息检索方法，这些应用对公司来说可能会变得更有价值。