Relations Prediction for Knowledge Graph Completion using Large Language Models

• URL: http://arxiv.org/abs/2405.02738v1
• Date: Sat, 4 May 2024 19:04:51 GMT
• Title: Relations Prediction for Knowledge Graph Completion using Large Language Models
• Authors: Sakher Khalil Alqaaidi, Krzysztof Kochut,
• Abstract summary: We make use of the knowledge graph node names to fine-tune a large language model for the relation prediction task. Our experiments show that we accomplish new scores on a widely used knowledge graph benchmark.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Graphs have been widely used to represent facts in a structured format. Due to their large scale applications, knowledge graphs suffer from being incomplete. The relation prediction task obtains knowledge graph completion by assigning one or more possible relations to each pair of nodes. In this work, we make use of the knowledge graph node names to fine-tune a large language model for the relation prediction task. By utilizing the node names only we enable our model to operate sufficiently in the inductive settings. Our experiments show that we accomplish new scores on a widely used knowledge graph benchmark.

Related papers

• Knowledge Graph Completion using Structural and Textual Embeddings [0.0]
  We propose a relations prediction model that harnesses both textual and structural information within Knowledge Graphs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.
  arXiv  Detail & Related papers  (2024-04-24T21:04:14Z)
• Exploring Large Language Models for Knowledge Graph Completion [17.139056629060626]
  We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction.
  arXiv  Detail & Related papers  (2023-08-26T16:51:17Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Graph Learning and Its Advancements on Large Language Models: A Holistic Survey [37.01696685233113]
  This survey focuses on the most recent advancements in integrating graph learning with pre-trained language models. We provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
  arXiv  Detail & Related papers  (2022-12-17T22:05:07Z)
• KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction [0.0]
  We introduce a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types. We show that further pre-training the language models with this additional embedding layer using the triples extracted from the knowledge graph, followed by the standard fine-tuning phase sets a new state-of-the-art performance for the link prediction task on the benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-04T20:38:12Z)
• Joint Language Semantic and Structure Embedding for Knowledge Graph Completion [66.15933600765835]
  We propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models. Our experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method.
  arXiv  Detail & Related papers  (2022-09-19T02:41:02Z)
• Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning [84.35102534158621]
  We study pre-trained language models that generate explanation graphs in an end-to-end manner. We propose simple yet effective ways of graph perturbations via node and edge edit operations. Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs.
  arXiv  Detail & Related papers  (2022-04-11T00:58:27Z)
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation [74.14009444678031]
  We propose Knowledge-aware Conditional Attention Networks (KCAN) to incorporate knowledge graph into a recommender system. We use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations.
  arXiv  Detail & Related papers  (2021-11-03T09:40:43Z)
• Cross-Network Learning with Partially Aligned Graph Convolutional Networks [21.458260469446863]
  I propose partially aligned graph convolutional networks to learn node representations across the models. Experiments on real-world knowledge graphs and collaboration networks show the superior performance of our proposed methods.
  arXiv  Detail & Related papers  (2021-06-03T04:07:26Z)
• ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning [65.15423587105472]
  We present a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict whether the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. A significant 83% of our graphs contain external commonsense nodes with diverse structures and reasoning depths.
  arXiv  Detail & Related papers  (2021-04-15T17:51:36Z)
• Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs [96.73259297063619]
  We consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions. We leverage Generative Adrial Networks (GANs) to establish the connection between text and knowledge graph domain.
  arXiv  Detail & Related papers  (2020-01-08T01:19:08Z)

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.