Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction

Open-domain Relational Triplet Extraction (ORTE) is the foundation for mining structured knowledge without predefined schemas. Despite the impressive in-context learning capabilities of Large Language Models (LLMs), existing methods are hindered by their reliance on static, heuristic-driven prompting strategies. Due to the lack of reflection mechanisms required to internalize erroneous signals, these methods exhibit vulnerability in semantic ambiguity, often making erroneous extraction patterns permanent. To address this bottleneck, we propose a Knowledge Reconstruction-driven Prompt Optimization (KRPO) framework to assist LLMs in continuously improving their extraction capabilities for complex ORTE task flows. Specifically, we design a self-evaluation mechanism based on knowledge restoration, which provides intrinsic feedback signals by projecting structured triplets into semantic consistency scores. Subsequently, we propose a prompt optimizer based on a textual gradient that can internalize historical experiences to iteratively optimize prompts, which can better guide LLMs to handle subsequent extraction tasks. Furthermore, to alleviate relation redundancy, we design a relation canonicalization memory that collects representative relations and provides semantically distinct schemas for the triplets. Extensive experiments across three datasets show that KRPO significantly outperforms strong baselines in the extraction F1 score.

Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs

Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models' performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP's effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug-and-play method improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods.