Schema-adaptable Knowledge Graph Construction (2023)

Abstract: 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.

## introduction knowledge graph construction (kgc), typically through information extraction, has enjoyed widespread empirical success and can provide backend support for various nlp tasks, such as question answering @xcite @xcite , commonsense reasoning @xcite etc. traditional kgc tasks, including named entity recognition (ner) @xcite , relation extraction (re) @xcite @xcite and event extraction (ee) @xcite @xcite are "reactive", relying on static pre-defined schema from end-users. however, as shown in figure 1 , the schema may evolve along with scenario adaptation, making previous models challenging to utilize without re-training. note that existing information extraction systems can only handle a fixed number of classes by pre-defined schema and performing once-and-forall training on a fixed framework. it is desirable to respond to changes (e.g., evolving schema) to existing kgs, making the system act more "proactively" like humans who can handle flexible knowledge updates. early, several approaches introduce incremental learning @xcite @xcite to learn new classes continually. in this case, the extraction system learns from the class incremental data stream but usually suffers significant performance degradation on the old class when adapting to the new class. stated differently, previous studies put emphasis on struggling against catastrophic forgetting @xcite . however, for the schemaevolving scenarios, the dynamic generalizability of extraction models plays a vital role and needs to be inspected from an ontology evolution perspective. therefore, we propose a novel kgc task dubbed schema-adaptable kgc, where the models are required to have the ability to represent and adapt to complement knowledge extraction. we first construct datasets according to three principles of evolutionary schema directions (horizontal schema expansion, vertical schema expansion, and hybrid schema expansion) on three tasks of ner, re 2 , and ee. through empirical analysis, we notice that approaches of text2event @xcite , tanl @xcite , uie @xcite , and gpt-3.5 @xcite cannot effectively extract the information given complex evolving schema. we argue that the major issues lie in the following: 1) how to learn dynamic and generalizable schema representations as conditions for extraction; 2) how to precisely extract new instances constrained with newly updated schema. to this end, we propose a simple baseline dubbed adakgc, which introduces schemaenriched prefix instructors (spi) to represent and transfer the learnable schema-specific knowledge. at each iteration stage, we linearly convert from the current schema graph to learnable prompts, initialized with the ontology name and connected to taskspecific prefixes. to encourage the decoder to understand the dynamic schema, we utilize a schemaconditioned dynamic decoding (sdd) strategy that constructs a decoding path of schema-specific vocabulary to the output space. when the schema changes, we dynamically construct a new trie-tree to adjust the output space. note that adakgc is model agnostic and can handle a variety of challenging schema evolution scenarios. we summarize the contribution of this work as: ## conclusion this paper introduces a new task of schemaadaptable kgc with benchmark datasets and a new baseline adakgc. we illustrate the task difficulties with previous baselines on three principles of schema expansion patterns (horizontal, vertical, hybrid) and demonstrate the effectiveness of the proposed adakgc.