  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.
