As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential to improve the everyday life of the elderly and chronically ill people. This includes, for example, medication reminders, routine checks, or social chit-chat. In addition, conversational agents can satisfy the fundamental need of having access to information about daily news or local events, which enables individuals to stay informed and connected with the world around them. However, finding relevant news sources and navigating the plethora of news articles available online can be overwhelming, particularly for those who may have limited technological literacy or health-related impairments. To address this challenge, we propose an innovative solution that combines knowledge graphs and conversational agents for news search in assisted living. By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people to easily discover relevant news articles and give personalized recommendations. We explain our design choices, provide a system architecture, share insights of an initial user test, and give an outlook on planned future work.

翻译：随着医疗保健领域面临人口老龄化、人员短缺及常见慢性病等重大挑战，为个体提供高质量护理已变得极为困难。对话代理已被证明是缓解这些问题的有前景技术——以数字健康助手的形式，它们有望改善老年人和慢性病患者的生活质量，包括用药提醒、常规检查或社交闲聊等功能。此外，对话代理还能满足人们获取日常新闻或本地活动信息的基本需求，帮助个体保持信息通达并与外界保持联系。然而，寻找相关新闻来源并筛选海量在线新闻文章可能令人应接不暇，尤其是对技术素养有限或存在健康障碍的人群而言。为应对这一挑战，我们提出了一种创新解决方案，将知识图谱与对话代理相结合，用于辅助生活场景中的新闻搜索。通过利用图数据库对新闻数据进行语义结构化处理，并实现基于语音的直观交互界面，我们的系统能够帮助需要护理的个体轻松发现相关新闻文章，并提供个性化推荐。本文阐述了设计思路、系统架构，分享了初步用户测试的见解，并对未来工作进行了展望。