Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to dynamic scenarios or domains, whereas a new type of knowledge emerges. This necessitates a system that can handle evolving schema automatically to extract information for KGC. To address this need, we propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training. We first split and convert existing datasets based on three principles to build a benchmark, i.e., horizontal schema expansion, vertical schema expansion, and hybrid schema expansion; then investigate the schema-adaptable performance of several well-known approaches such as Text2Event, TANL, UIE and GPT-3. We further propose a simple yet effective baseline dubbed AdaKGC, which contains schema-enriched prefix instructor and schema-conditioned dynamic decoding to better handle evolving schema. Comprehensive experimental results illustrate that AdaKGC can outperform baselines but still have room for improvement. We hope the proposed work can deliver benefits to the community. Code and datasets will be available in https://github.com/zjunlp/AdaKGC.

翻译：传统的知识图谱构建（KGC）方法通常遵循静态信息抽取范式，基于预定义的封闭式模式集合。因此，这类方法在应用于需要处理新兴知识类型的动态场景或领域时存在不足。这要求系统能够自动处理演化的模式，以完成KGC的信息抽取工作。为满足这一需求，我们提出名为"可适应模式KGC"的新任务，旨在无需重新训练的情况下，基于动态变化的模式图持续抽取实体、关系和事件。我们首先根据三个原则对现有数据集进行拆分与转换以构建基准：水平模式扩展、垂直模式扩展和混合模式扩展；随后探究了Text2Event、TANL、UIE和GPT-3等若干知名方法的模式适应性能。我们进一步提出一个简洁有效的基线模型AdaKGC，其包含模式增强的前缀指导器和模式条件化的动态解码机制，以更有效地处理演化的模式。综合实验结果表明，AdaKGC能够超越基线方法，但仍有改进空间。期望本工作能为学界带来助益。代码与数据集将发布于https://github.com/zjunlp/AdaKGC。