Online encyclopedia such as Wikipedia has become one of the best sources of knowledge. Much effort has been devoted to expanding and enriching the structured data by automatic information extraction from unstructured text in Wikipedia. Although remarkable progresses have been made, their effectiveness and efficiency is still limited as they try to tackle an extremely difficult natural language understanding problems and heavily relies on supervised learning approaches which require large amount effort to label the training data. In this paper, instead of performing information extraction over unstructured natural language text directly, we focus on a rich set of semi-structured data in Wikipedia articles: linked entities. The idea of this paper is the following: If we can summarize the relationship between the entity and its linked entities, we immediately harvest some of the most important information about the entity. To this end, we propose a novel rank aggregation approach to remove noise, an effective clustering and labeling algorithm to extract knowledge.
AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Alibaba Cloud Computing Co. Ltd.（阿里云计算有限公司）
Who's important? -- SUnSET: Synergistic Understanding of Stakeholder, Events and Time for Timeline Generation
National University of Singapore（国立新加坡大学）；A*STAR Institute of High Performance Computing（A*STAR高性能计算研究所）；Celia Research Team（Celia研究团队）；Norbert Wiener Research Center, Huawei International（诺伯特·维纳研究中心，华为国际）
EICopilot: Search and Explore Enterprise Information over Large-scale Knowledge Graphs with LLM-driven Agents
Baidu Inc.（百度公司）；Wuhan University（武汉大学）；University of Chinese Academy of Sciences（中国科学院大学）；South China University of Technology（华南理工大学）；University of Macau（澳门大学）
