SAKA平台让普通人也能半自动构建知识图谱，还能从音频中提取信息并问答。
SAKA: An Intelligent Platform for Semi-automated Knowledge Graph Construction and Application
- 用户通过交互式操作，半自动构建多版本知识图谱。
- 提出音频信息抽取方法（AGIE），可将语音数据转为知识图谱。
- 基于用户构建的图谱实现语义解析问答系统，提升实用性。
知识图谱技术广泛应用于多个领域，众多公司已基于知识图谱推出应用。然而，现有知识图谱平台大多需用户具备专业知识，并耗费大量时间手动构建记录，对普通用户不友好。此外，音频数据虽丰富且蕴含重要信息，但难以转化为知识图谱。同时，平台通常未能充分发挥用户构建的知识图谱潜力。本文提出一个智能、易用的半自动知识图谱构建与应用平台（SAKA），以解决上述问题。首先，用户可通过与平台交互，半自动地从多领域结构化数据中构建知识图谱，并支持多版本存储、查看、管理与更新。其次，提出一种基于音频的知识图谱信息抽取方法（AGIE），实现从音频数据构建知识图谱。最后，平台基于用户创建的知识图谱构建了基于语义解析的知识库问答系统（KBQA）。实验证明了SAKA平台在半自动知识图谱构建上的可行性。
原文摘要 · Abstract (English)
Knowledge graph (KG) technology is extensively utilized in many areas, and many companies offer applications based on KG. Nonetheless, most KG platforms necessitate expertise and tremendous time and effort from users to construct KG records manually, which poses great difficulties for ordinary people. Additionally, audio data is abundant and holds valuable information, but it is challenging to transform it into a KG. What's more, the platforms usually do not leverage the full potential of the KGs constructed by users. In this paper, we propose an intelligent and user-friendly platform for Semi-automated KG Construction and Application (SAKA) to address the aforementioned problems. Primarily, users can semi-automatically construct KGs from structured data of numerous areas by interacting with the platform, based on which multi-versions of KG can be stored, viewed, managed, and updated. Moreover, we propose an Audio-based KG Information Extraction (AGIE) method to establish KGs from audio data. Lastly, the platform creates a semantic parsing-based knowledge base question answering (KBQA) system based on the user-created KGs. We prove the feasibility of the semi-automatic KG construction method on the SAKA platform.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。