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-3.5. We further propose a simple yet effective baseline dubbed \textsc{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 available at https://github.com/zjunlp/AdaKGC.

翻译：传统知识图谱构建方法通常遵循静态信息抽取范式，使用预定义的封闭模式集合。因此，当应用于动态场景或新兴知识涌现的领域时，此类方法存在明显不足。这要求系统能够自动处理不断演化的模式，以抽取信息用于知识图谱构建。为应对这一需求，我们提出名为"模式自适应知识图谱构建"的新任务，旨在基于动态变化的模式图持续抽取实体、关系和事件，且无需重新训练。我们首先依据三条原则对现有数据集进行拆分和转换以构建基准：水平模式扩展、垂直模式扩展和混合模式扩展；进而研究Text2Event、TANL、UIE和GPT-3.5等知名方法的模式自适应性能。我们进一步提出简洁有效的基线方法AdaKGC，其包含模式增强前缀指导器和模式条件动态解码机制，可更优地处理演化中的模式。综合实验结果表明，AdaKGC能超越基线方法，但仍存在改进空间。希望本研究能为社区带来裨益。代码与数据集访问地址：https://github.com/zjunlp/AdaKGC。