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Making Large Language Models Perform Better in Knowledge Graph Completion
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Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencies, overlooking critical structural information integral to KGs. In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning. We first discuss on the existing LLM paradigms like in-context learning and instruction tuning, proposing basic structural information injection approaches. Then we propose a Knowledge Prefix Adapter (KoPA) to fulfill this stated goal. The KoPA uses a structural pre-training phase to comprehend the intricate entities and relations within KGs, representing them as structural embeddings. Then KoPA communicates such cross-modal structural information understanding to the LLMs through a knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens positioned as a prefix of the input prompt. We conduct comprehensive experiments and provide incisive analysis concerning how the introduction of cross-modal structural information would be better for LLM's factual knowledge reasoning ability. Our code and data are available at https://github.com/zjukg/KoPA .
Forward citations
Cited by 7 Pith papers
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Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
SAT uses hierarchical contrastive alignment and a unified graph instruction to tune a lightweight adapter for knowledge graph completion, reporting large link prediction gains.
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K-ON: Stacking Knowledge On the Head Layer of Large Language Model
K-ON stacks K prediction heads onto an LLM to generate entity tokens in one step and uses entity-level contrastive learning, achieving new state-of-the-art results on two knowledge graph completion benchmarks.
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An Automatic Graph Construction Framework based on Large Language Models for Recommendation
AutoGraph uses LLM semantic vectors, residual vector quantization, and metapath GAT propagation to construct an automatic graph that improves recommendation across four backbones and three datasets.
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Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion
LeSR uses an LLM to propose logic rules from sampled subgraphs, then a trainable reasoner weights those rules against the knowledge base and combines them with RotatE for KBC.
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KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models
A bidirectional decoder with a graph-aware attention mask, knowledge-masked prediction, and contrastive sub-graph alignment achieves reported state-of-the-art link prediction on Wikidata5M and competitive results on WN18RR.
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A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects
A task-oriented survey of knowledge graph reasoning, covering six task categories, benchmark datasets, downstream applications, and future challenges.
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From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies
A review that organizes the knowledge graph and large language model integration field into three categories and argues for more attention to scalability, efficiency, and data quality.
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