Abstract
Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from news and social media online interactions, through open access scholarly communications and observational data in the form of digital health records and online drug reviews. The volume and variety of data across all this range of domains has created both unprecedented opportunities and pressing challenges for extracting actionable knowledge for several application scenarios. However, the extraction of rich semantic knowledge demands the deployment of scalable and flexible automatic methods adaptable across text genres and schema specifications. Moreover, the full potential of these data can only be unlocked by coupling information extraction methods with Semantic Web techniques for the construction of full-fledged Knowledge Graphs, that are semantically transparent, explainable by design and interoperable. In this thesis, we experiment with the application of Natural Language Processing, Machine Learning and Generative AI methods, powered by Semantic Web best practices, to the automatic construction of Knowledge Graphs from large text corpora, in three use case applications: the analysis of the Digital Transformation discourse in the global news and social media platforms; the mapping and trend analysis of recent research in the Architecture, Engineering, Construction and Operations domain from a large corpus of publications; the generation of causal relation graphs of biomedical entities from electronic health records and patient-authored drug reviews. The contributions of this thesis to the research community are in terms of benchmark evaluation results, the design of customized algorithms and the creation of data resources in the form of Knowledge Graphs, together with data analysis results built on top of them.
Abstract (translated)
社会几乎所有领域都正在经历非结构化文本数据量的大幅增长，这些数据从新闻和社交媒体在线互动中产生并发布，涵盖开放获取的学术交流、数字健康记录形式的观测数据以及患者撰写的药物评论。跨领域数据的庞大规模与多样性，为多个应用场景提取可操作知识带来了前所未有的机遇与紧迫挑战。然而，要提取丰富的语义知识，需要部署可扩展、灵活且能适应不同文本体裁与模式规范的自动方法。此外，这些数据的全部潜力唯有通过将信息提取方法与语义网技术相结合，构建出语义透明、可解释设计且可互操作的全功能知识图谱，才能得以释放。在本论文中，我们依托语义网最佳实践，实验性地将自然语言处理、机器学习与生成式人工智能方法应用于从大规模文本语料库自动构建知识图谱，具体包括三个应用案例：基于全球新闻与社交媒体平台分析数字化转型议题；从大量出版物中绘制并趋势分析建筑、工程、施工与运营领域的最新研究；从电子健康记录与患者撰写的药物评论中生成生物医学实体的因果关系图谱。本论文对研究界的贡献体现在基准评估结果、定制化算法设计以及以知识图谱形式创建的数据资源，并基于这些资源产出数据分析结果。
URL
https://arxiv.org/abs/2603.25862
