知识图谱是llm驱动的企业问题回答的信任来源

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics Pub Date : 2025-01-15 DOI:10.1016/j.websem.2024.100858

Juan Sequeda, Dean Allemang, Bryon Jacob

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引用次数: 0

摘要

生成式人工智能提供了一种创新和令人兴奋的方式来管理任何规模的知识和数据；对于小型项目，在企业级，甚至在万维网规模。人们很容易认为生成式人工智能已经使其他基于知识的技术过时了；我们想用基于知识的系统、知识图甚至专家系统做的任何事情都可以用生成式人工智能来完成。我们的立场与这一结论相反。我们使用生成式人工智能实现企业问答系统的实践经验表明，知识图谱以多种方式支持这种基础架构：它们提供了一个正式的框架来评估LLM生成的查询的有效性，作为解释结果的基础，并提供对受治理和可信数据的访问。在这份立场文件中，我们分享了我们的经验，目前的行业需求，并概述了未来研究贡献的机会。

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Knowledge Graphs as a source of trust for LLM-powered enterprise question answering

Generative AI provides an innovative and exciting way to manage knowledge and data at any scale; for small projects, at the enterprise level, and even at a world wide web scale. It is tempting to think that Generative AI has made other knowledge-based technologies obsolete; that anything we wanted to do with knowledge-based systems, Knowledge Graphs or even expert systems can instead be done with Generative AI. Our position is counter to that conclusion.

Our practical experience on implementing enterprise question answering systems using Generative AI has shown that Knowledge Graphs support this infrastructure in multiple ways: they provide a formal framework to evaluate the validity of a query generated by an LLM, serve as a foundation for explaining results, and offer access to governed and trusted data. In this position paper, we share our experience, present industry needs, and outline the opportunities for future research contributions.

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来源期刊

Journal of Web Semantics 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.00%

发文量

审稿时长

14.6 weeks

期刊介绍： The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.