Comprehending Knowledge Graphs with Large Language Models for Recommender Systems

• URL: http://arxiv.org/abs/2410.12229v1
• Date: Wed, 16 Oct 2024 04:44:34 GMT
• Title: Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
• Authors: Ziqiang Cui, Yunpeng Weng, Xing Tang, Fuyuan Lyu, Dugang Liu, Xiuqiang He, Chen Ma,
• Abstract summary: We propose a novel method called CoLaKG, which leverages large language models for knowledge-aware recommendation. We first extract subgraphs centered on each item from the KG and convert them into textual inputs for the LLM. The LLM then outputs its comprehension of these item-centered subgraphs, which are subsequently transformed into semantic embeddings.
• Score: 13.270018897057293
• License:
• Abstract: Recently, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffer from missing facts or limited scopes. This can lead to biased knowledge representations, thereby constraining the model's performance. Second, existing methods typically convert textual information into IDs, resulting in the loss of natural semantic connections between different items. Third, existing methods struggle to capture high-order relationships in global KGs due to their inefficient layer-by-layer information propagation mechanisms, which are prone to introducing significant noise. To address these limitations, we propose a novel method called CoLaKG, which leverages large language models (LLMs) for knowledge-aware recommendation. The extensive world knowledge and remarkable reasoning capabilities of LLMs enable them to supplement KGs. Additionally, the strong text comprehension abilities of LLMs allow for a better understanding of semantic information. Based on this, we first extract subgraphs centered on each item from the KG and convert them into textual inputs for the LLM. The LLM then outputs its comprehension of these item-centered subgraphs, which are subsequently transformed into semantic embeddings. Furthermore, to utilize the global information of the KG, we construct an item-item graph using these semantic embeddings, which can directly capture higher-order associations between items. Both the semantic embeddings and the structural information from the item-item graph are effectively integrated into the recommendation model through our designed representation alignment and neighbor augmentation modules. Extensive experiments on four real-world datasets demonstrate the superiority of our method.

Related papers

• Can LLMs be Good Graph Judger for Knowledge Graph Construction? [33.958327252291]
  In this paper, we propose GraphJudger, a knowledge graph construction framework to address the aforementioned challenges. We introduce three innovative modules in our method, which are entity-centric iterative text denoising, knowledge aware instruction tuning and graph judgement. Experiments conducted on two general text-graph pair datasets and one domain-specific text-graph pair dataset show superior performances compared to baseline methods.
  arXiv  Detail & Related papers  (2024-11-26T12:46:57Z)
• Combining Knowledge Graphs and Large Language Models [4.991122366385628]
  Large language models (LLMs) show astonishing results in language understanding and generation. They still show some disadvantages, such as hallucinations and lack of domain-specific knowledge. These issues can be effectively mitigated by incorporating knowledge graphs (KGs) This work collected 28 papers outlining methods for KG-powered LLMs, LLM-based KGs, and LLM-KG hybrid approaches.
  arXiv  Detail & Related papers  (2024-07-09T05:42:53Z)
• Knowledge Graph-Enhanced Large Language Models via Path Selection [58.228392005755026]
  Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. LLMs are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. We propose a principled framework KELP with three stages to handle the above problems.
  arXiv  Detail & Related papers  (2024-06-19T21:45:20Z)
• Knowledge Graph Large Language Model (KG-LLM) for Link Prediction [43.55117421485917]
  We introduce the Knowledge Graph Large Language Model (KG-LLM), a novel framework that leverages large language models (LLMs) for knowledge graph tasks. We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs. To show the efficacy of the KG-LLM Framework, we fine-tune three leading LLMs within this framework, including Flan-T5, LLaMa2 and Gemma.
  arXiv  Detail & Related papers  (2024-03-12T04:47:29Z)
• Large Language Models Can Better Understand Knowledge Graphs Than We Thought [13.336418752729987]
  knowledge graph (KG) embeddings with model parameters become increasingly costly. Current prompting methods often rely on a trial-and-error approach. We show that unordered linearized triples are more effective for LLMs' understanding of KGs compared to fluent NL text.
  arXiv  Detail & Related papers  (2024-02-18T10:44:03Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Unifying Large Language Models and Knowledge Graphs: A Roadmap [61.824618473293725]
  Large language models (LLMs) are making new waves in the field of natural language processing and artificial intelligence. Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge.
  arXiv  Detail & Related papers  (2023-06-14T07:15:26Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• Empowering Language Models with Knowledge Graph Reasoning for Question Answering [117.79170629640525]
  We propose knOwledge REasOning empowered Language Model (OREO-LM) OREO-LM consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs. We show significant performance gain, achieving state-of-art results in the Closed-Book setting.
  arXiv  Detail & Related papers  (2022-11-15T18:26:26Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)
• KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs [26.557447199727758]
  We propose a novel knowledge-aware language model framework based on fine-tuning process. Our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT.
  arXiv  Detail & Related papers  (2021-09-09T12:39:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.