Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks

• URL: http://arxiv.org/abs/2405.13085v1
• Date: Tue, 21 May 2024 08:22:14 GMT
• Title: Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
• Authors: Yichi Zhang, Binbin Hu, Zhuo Chen, Lingbing Guo, Ziqi Liu, Zhiqiang Zhang, Lei Liang, Huajun Chen, Wen Zhang,
• Abstract summary: Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale Knowledge graphs (KGs) MuDoK is a plug-and-play prompt learning approach that can be adapted to different downstream task backbones. Our framework brings significant performance gains, along with its generality, efficiency, and transferability.
• Score: 48.102084345907095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enhance different downstream tasks, which is a key direction for KG management, maintenance, and applications. Existing works often focus on purely research questions in open domains, or they are not open source due to data security and privacy in real scenarios. Meanwhile, existing studies have not explored the training efficiency and transferability of KGP models in depth. To address these problems, We propose a framework MuDoK to achieve multi-domain collaborative pre-training and efficient prefix prompt tuning to serve diverse downstream tasks like recommendation and text understanding. Our design is a plug-and-play prompt learning approach that can be flexibly adapted to different downstream task backbones. In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research. We evaluated our approach based on constructed KPI benchmarks using diverse backbone models in heterogeneous downstream tasks. The experimental results show that our framework brings significant performance gains, along with its generality, efficiency, and transferability.

Related papers

• Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering [0.0]
  In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA) We propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework.
  arXiv  Detail & Related papers  (2024-08-24T19:34:04Z)
• Prompt-Based Spatio-Temporal Graph Transfer Learning [22.855189872649376]
  We propose a prompt-based framework capable of adapting to multi-diverse tasks in a data-scarce domain. We employ learnable prompts to achieve domain and task transfer in a two-stage pipeline. Our experiments demonstrate that STGP outperforms state-of-the-art baselines in three tasks-forecasting, kriging, and extrapolation-achieving an improvement of up to 10.7%.
  arXiv  Detail & Related papers  (2024-05-21T02:06:40Z)
• ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt [67.8934749027315]
  We propose a unified framework for graph hybrid pre-training which injects the task identification and position identification into GNNs. We also propose a novel pre-training paradigm based on a group of $k$-nearest neighbors.
  arXiv  Detail & Related papers  (2023-10-23T12:11:13Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers [140.0479479231558]
  In this work, we aim to unify a variety of pre-training tasks into a multi-task pre-trained model, namely MASTER. MASTER utilizes a shared-encoder multi-decoder architecture that can construct a representation bottleneck to compress the abundant semantic information across tasks into dense vectors.
  arXiv  Detail & Related papers  (2022-12-15T13:57:07Z)
• Hierarchically Structured Task-Agnostic Continual Learning [0.0]
  We take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle. We propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths. Our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms.
  arXiv  Detail & Related papers  (2022-11-14T19:53:15Z)
• Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation [51.21190751266442]
  Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data. By learning from large-scale unlabeled samples, self-supervised learning has now become a new trend in deep learning. We propose a novel textbfSelf-textbfSupervised textbfGraph Neural Network (SSG) to enable more effective inter-task information exchange and knowledge sharing.
  arXiv  Detail & Related papers  (2022-04-08T03:37:56Z)
• Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially Observable Environments [9.067091068256747]
  We propose a novel network structure called hierarchical graph recurrent network(HGRN) for multi-agent cooperation under partial observability. Based on the above technologies, we proposed a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant named SAC-HRGN.
  arXiv  Detail & Related papers  (2021-09-05T09:51:25Z)
• Elastic Architecture Search for Diverse Tasks with Different Resources [87.23061200971912]
  We study a new challenging problem of efficient deployment for diverse tasks with different resources, where the resource constraint and task of interest corresponding to a group of classes are dynamically specified at testing time. Previous NAS approaches seek to design architectures for all classes simultaneously, which may not be optimal for some individual tasks. We present a novel and general framework, called Elastic Architecture Search (EAS), permitting instant specializations at runtime for diverse tasks with various resource constraints.
  arXiv  Detail & Related papers  (2021-08-03T00:54:27Z)

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.