In recent years, multi-modal entity linking (MEL) has garnered increasing
attention in the research community due to its significance in numerous
multi-modal applications. Video, as a popular means of information
transmission, has become prevalent in people's daily lives. However, most
existing MEL methods primarily focus on linking textual and visual mentions or
offline videos's mentions to entities in multi-modal knowledge bases, with
limited efforts devoted to linking mentions within online video content. In
this paper, we propose a task called Online Video Entity Linking OVEL, aiming
to establish connections between mentions in online videos and a knowledge base
with high accuracy and timeliness. To facilitate the research works of OVEL, we
specifically concentrate on live delivery scenarios and construct a live
delivery entity linking dataset called LIVE. Besides, we propose an evaluation
metric that considers timelessness, robustness, and accuracy. Furthermore, to
effectively handle OVEL task, we leverage a memory block managed by a Large
Language Model and retrieve entity candidates from the knowledge base to
augment LLM performance on memory management. The experimental results prove
the effectiveness and efficiency of our method.

近年来，多模态实体链接（MEL）备受研究界关注，由于其在众多多模态应用中的重要性。本文提出了一项名为在线视频实体链接（OVEL）的任务，旨在建立在线视频中提及的内容与知识库之间的准确和及时的连接。为了促进 OVEL 的研究工作，我们针对直播传输场景构建了一个称为 LIVE 的直播传输实体链接数据集。此外，我们提出了一个考虑到时间性、稳定性和准确性的评价指标。为了有效处理 OVEL 任务，我们利用由大型语言模型管理的内存块，从知识库中检索实体候选项，以增强 LLM 在内存管理方面的性能。实验结果证明了我们方法的有效性和效率。