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 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领域时，此类方法表现不足，亟需能够自动处理演进模式以完成知识图谱信息抽取的系统。针对该需求，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，无需重新训练。我们首先基于三项原则（水平模式扩展、垂直模式扩展和混合模式扩展）拆分并转换现有数据集以构建基准测试集，随后研究Text2Event、TANL、UIE和GPT-3.5等若干知名方法的模式自适应性能。我们进一步提出名为AdaKGC的简洁有效基线方法，该方法包含模式增强前缀指导器和模式条件动态解码机制，能更好处理演进模式。综合实验结果表明，AdaKGC可超越各基线方法，但仍存在改进空间。希望所提出工作能为社区带来助益。代码和数据集将在https://github.com/zjunlp/AdaKGC开源。