Developing a knowledge inference and reasoning engine to extract meaningful insights from unstructured data using a novel neighbourhood graph approach

开发知识推理和推理引擎，使用新颖的邻域图方法从非结构化数据中提取有意义的见解

• 批准号: 10027139
• 金额: $ 35.37万
• 依托单位: AUQUAN LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10027139
• 关键词: Developing; knowledge; inference; reasoning; engine

Information overload is a common problem, particularly in data heavy industries like finance. Unstructured text, such as company reports, news, transcripts, emails and memos, contain much valuable information that is often missed by data analysts and key decision makers, for example when assessing risk and investments. Current solutions do not visually display the links in datasets that could help decision makers make better, more informed decisions.Auquan is solving a significant, unmet, global need by developing technology that visually links relevant, but often hidden, insights without overwhelming the user with high volumes of data. Our solution extracts information from both unstructured and structured datasets, leading to better decisions and more productive analysts. Automating information retrieval from unstructured datasets applies to many sectors, not just finance. It is particularly challenging because it requires custom extraction (e.g. differentiating between tables/text) from different data formats (e.g. presentations/PDFs/reports) and domain-specific (e.g. legal language versus financial text) training of NLP algorithms.This project aims to dramatically extend and improve our technology, then undertake pilot evaluations with early adopters to measure/demonstrate that our actionable insights save time and deliver better performance. We also plan to investigate exciting wider use-cases for our technology outside the finance sector including scientific/medical literature.

信息过载是一个常见的问题，特别是在金融等数据密集型行业。非结构化文本，如公司报告、新闻、文字记录、电子邮件和备忘录，包含许多有价值的信息，这些信息经常被数据分析师和关键决策者遗漏，例如在评估风险和投资时。目前的解决方案不能直观地显示数据集中的链接，这些链接可以帮助决策者做出更好、更明智的决策。Auquan正在通过开发技术来解决一个重要的、未得到满足的全球需求，该技术可以在视觉上链接相关但往往隐藏的见解，而不会用大量数据淹没用户。我们的解决方案从非结构化和结构化数据集中提取信息，从而实现更好的决策和更高效的分析师。从非结构化数据集中自动检索信息适用于许多行业，而不仅仅是金融业。它特别具有挑战性，因为它需要从不同的数据格式(例如演示文稿/PDF/报告)中自定义提取(例如区分表格/文本)和特定领域的NLP算法培训(例如法律语言与金融文本)。该项目旨在显著扩展和改进我们的技术，然后与早期采用者进行试点评估，以衡量/证明我们可操作的见解节省了时间并提供了更好的性能。我们还计划调查我们的技术在金融部门以外令人兴奋的更广泛的使用案例，包括科学/医学文献。