[2402.15048] Unlocking the Power of Large Language Models for Entity Alignment

View a PDF of the paper titled Unlocking the Energy of Giant Language Fashions for Entity Alignment, by Xuhui Jiang and eight different authors

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Summary:Entity Alignment (EA) is significant for integrating various data graph (KG) knowledge, enjoying a vital position in data-driven AI purposes. Conventional EA strategies primarily depend on evaluating entity embeddings, however their effectiveness is constrained by the restricted enter KG knowledge and the capabilities of the illustration studying methods. In opposition to this backdrop, we introduce ChatEA, an modern framework that comes with massive language fashions (LLMs) to enhance EA. To deal with the constraints of restricted enter KG knowledge, ChatEA introduces a KG-code translation module that interprets KG buildings right into a format comprehensible by LLMs, thereby permitting LLMs to make the most of their in depth background data to enhance EA accuracy. To beat the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA technique that capitalizes on LLMs’ functionality for multi-step reasoning in a dialogue format, thereby enhancing accuracy whereas preserving effectivity. Our experimental outcomes confirm ChatEA’s superior efficiency, highlighting LLMs’ potential in facilitating EA duties.