Analyzing Mathematics to Detect Disguised Academic Plagiarism

该项目的目标是改进对伪装形式的学术剽窃的检测方法，例如释义、翻译和思想剽窃，特别是对科学、技术、工程和数学（STEM）领域的文件。为了实现这一目标，我们将进行基础研究，分析数学表达式作为文本独立和语言独立的功能，以确定潜在的可疑相似性的文件。我们将结合联合收割机的新的基于语法的方法来检测剽窃与基于文本和基于引用的检测方法，我们在以前的研究中开发。综合考虑不同特征类型的相似性评估方法是最有前途的检测范围广泛的学术剽窃形式。为了使基于数学的方法，剽窃检测，以及在实践中的其他信息检索任务，我们将研究从各种输入格式的科学文献中提取和语义增强数学表达式的方法。对于这项研究任务，项目组将密切合作，与贝拉吉普的DFG资助的项目GI 1259/1-1“方法和工具，以促进检索的数学知识从数字图书馆的搜索，推荐和援助系统”。该项目开发数学信息检索方法，以满足维基百科的信息需求。拟议的项目将扩展这些方法并使其适用于科学文献。检测学术抄袭是一个紧迫的问题，例如，面向教育和研究机构、资助机构和学术出版商。然而，目前，剽窃检测提供商专注于识别学生中普遍存在的非混淆形式的剽窃。出于经济原因，他们避免了必要的研究和开发工作，以使检测更强烈的混淆形式的剽窃，这是研究论文的特点。为了使研究社区和剽窃检测提供商能够更好地识别精心伪装的学术剽窃实例，我们将在开源和免费使用剽窃检测系统HyPlag（www.hyplag.org）中实现我们的贡献，并公开提供我们研究的代码和数据。为了确保我们的研究满足领域专家的需求，我们将在zbMATH（www.zbMATH.org）专业数学编辑的帮助下不断评估我们的研究贡献。通过与数学专家密切合作，我们还打算这个项目奠定基础，为未来的贡献数学信息检索超越剽窃检测用例。

The goal of this project is to improve the detection methods for disguised forms of academic plagiarism, such as paraphrases, translations and idea plagiarism, particularly for documents in the fields of Science, Technology, Engineering and Mathematics (STEM). To achieve this goal, we will conduct fundamental research on analyzing mathematical expressions as text-independent and language-independent features to determine potentially suspicious similarity of documents. We will combine the novel mathematics-based approach to plagiarism detection with text-based and citation-based detection approaches that we developed in previous research. Combined approaches that consider different feature types for similarity assessment are most promising to detect the wide range of academic plagiarism forms. To enable the mathematics-based approach to plagiarism detection, as well as other information retrieval tasks in practice, we will research methods for extracting and semantically augmenting mathematical expressions from scientific documents in various input formats. For this research task, the project team will collaborate closely with the Bela Gipp’s DFG-funded project GI 1259/1-1 “Methods and Tools to Advance the Retrieval of Mathematical Knowledge from Digital Libraries for Search-, Recommendation- and Assistance-Systems”. The project develops mathematical information retrieval methods to serve information needs within the Wikipedia. The proposed project will extend these methods and make them applicable for scientific documents.Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. However, currently, plagiarism detection providers focus on identifying the non-obfuscated forms of plagiarism prevalent among students. For economic reasons, they avoid the research and development effort necessary to enable the detection of more strongly obfuscated forms of plagiarism, which are characteristic of research papers. To enable the research community and plagiarism detection providers to better identify carefully disguised instances of academic plagiarism, we will implement our contributions in the open source and free to use plagiarism detection system HyPlag (www.hyplag.org) and openly provide the code and data of our research. To ensure our research meets the needs of domain-experts, we will continuously evaluate our research contributions with the help of professional mathematics editors from zbMATH (www.zbMATH.org). By collaborating closely with mathematics experts, we furthermore intend this project to lay the groundwork for future contributions in Mathematical Information Retrieval beyond the plagiarism detection use case.