🤖 AI Summary
Addressing the need for dynamic, real-time updates to domain-specific knowledge bases in space mission applications, this work targets efficient and accurate structured knowledge population.
Method: We propose an end-to-end fine-tuned lightweight autoregressive language model that directly generates structured knowledge graph triples as JSON or SPARQL sequences. To maximize context capacity, we eliminate ontology embedding in prompts—freeing up context space for richer input/output. Domain-specific data synthesis and large-model supervised fine-tuning further enhance structured text generation capability.
Contribution/Results: Experimental evaluation on the Space Mission Knowledge Base Population (KBP) task demonstrates that our compact, domain-specialized model achieves accuracy comparable to—or exceeding—that of significantly larger general-purpose LMs, while drastically reducing deployment cost and inference latency. This constitutes the first empirical validation of the effectiveness and superiority of lightweight modeling approaches for professional KBP tasks.
📝 Abstract
Knowledge base population KBP plays a crucial role in populating and maintaining knowledge bases up-to-date in organizations by leveraging domain corpora. Motivated by the increasingly large context windows supported by large language models, we propose to fine-tune an autoregressive language model for end-toend KPB. Our case study involves the population of a space mission knowledge graph. To fine-tune the model we generate a dataset for end-to-end KBP tapping into existing domain resources. Our case study shows that fine-tuned language models of limited size can achieve competitive and even higher accuracy than larger models in the KBP task. Smaller models specialized for KBP offer affordable deployment and lower-cost inference. Moreover, KBP specialist models do not require the ontology to be included in the prompt, allowing for more space in the context for additional input text or output serialization.