由会话文本分析和自然语言处理驱动的自动地下知识问答检索引擎——阿布扎比资产管理大量文档的经验教训

F. Braik, Abdulla S. Al Shehhi, L. Saputelli, Carlos Mata, D. Badmaev, Salman Khan, Fariz Rahman
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引用次数: 0

摘要

本文的目的是交流一个名为“ASK Thamama”的创新概念的发展经验,该概念是由人工智能技术(包括文本分析和自然语言处理)驱动的自动数据和信息检索引擎。ASK是一个支持人工智能的会话搜索引擎,用于使用自然语言查询从各种内部数据存储库检索信息。文本处理和会话引擎概念是建立在可用的开源软件上的,需要最少的新库编码。使用包含1000个文档的数据集来验证关键功能,其搜索查询的准确率为90%,并能够为80%的查询提供特定的答案。这项工作的结果显示了令人鼓舞的结果,并证明了人工智能方法可以通过启用数据信息检索的自动化工作流程来提供自然语言搜索的价值。开发的人工智能方法在端到端知识管理工作流中具有巨大的集成潜力,可以利用可用的文档存储库获得有价值的见解,几乎不需要人工干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Subsurface Knowledge ASK Thamama Retrieval Engine Driven by Conversational Text Analytics and NLP - Lessons Learned in Managing Large Volume of Documents in Abu Dhabi Assets
The purpose of this paper is to communicate the experiences in the development of an innovative concept named "ASK Thamama" as an automated data and information retrieval engine driven by artificial intelligence techniques including text analytics and natural language processing. ASK is an AI enabled conversational search engine used to retrieve information from various internal data repositories using natural language queries. The text processing and conversational engine concept is built upon available open-source software requiring minimum coding of new libraries. A data set with 1000 documents was used to validate key functionalities with an accuracy of 90% of the search queries and able to provide specific answers for 80% of queries framed as questions. The results of this work show encouraging results and demonstrate value that AI-enabled methodologies can provide natural language search by enabling automated workflows for data information retrieval. The developed AI methodology has tremendous potential of integration in an end-to-end workflow of knowledge management by utilizing available document repositories to valuable insights, with little to no human intervention.
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