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

信息过载是一个普遍的问题，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、成绩单、电子邮件和备忘录，包含许多有价值的信息，这些信息往往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。目前的解决方案无法直观地显示数据集中的链接，从而帮助决策者做出更好、更明智的决策。Auquan正在开发一种技术，可以在视觉上链接相关但通常隐藏的见解，而不会让用户淹没在大量数据中，从而解决一个重要的、未满足的全球需求。我们的解决方案从非结构化和结构化数据集中提取信息，从而做出更好的决策并提高分析效率。从非结构化数据集自动检索信息适用于许多领域，而不仅仅是金融领域。它特别具有挑战性，因为它需要自定义提取（例如，区分表格/文本）（例如演示文稿/PDF/报告）和特定领域（例如法律的语言与金融文本）的NLP算法训练。该项目旨在极大地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/证明我们的可操作见解节省了时间并提供了更好的性能。我们还计划在金融领域之外为我们的技术研究更广泛的令人兴奋的用例，包括科学/医学文献。

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.