Towards a GML-Enabled Knowledge Graph Platform

• URL: http://arxiv.org/abs/2303.02166v1
• Date: Fri, 3 Mar 2023 17:41:11 GMT
• Title: Towards a GML-Enabled Knowledge Graph Platform
• Authors: Hussein Abdallah, Essam Mansour
• Abstract summary: This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a service on top of RDF engines. KGNet automates the training of GML models on a KG by identifying a task-specific subgraph. All trained models are accessible via a SPARQL-like query.
• Score: 0.5904265865319825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet automates the training of GML models on a KG by identifying a task-specific subgraph. This helps reduce the task-irrelevant KG structure and properties for better scalability and accuracy. While training a GML model on KG, KGNet collects metadata of trained models in the form of an RDF graph called KGMeta, which is interlinked with the relevant subgraphs in KG. Finally, all trained models are accessible via a SPARQL-like query. We call it a GML-enabled query and refer to it as SPARQLML. KGNet supports SPARQLML on top of existing RDF engines as an interface for querying and inferencing over KGs using GML models. The development of KGNet poses research opportunities in several areas, including meta-sampling for identifying task-specific subgraphs, GML pipeline automation with computational constraints, such as limited time and memory budget, and SPARQLML query optimization. KGNet supports different GML tasks, such as node classification, link prediction, and semantic entity matching. We evaluated KGNet using two real KGs of different application domains. Compared to training on the entire KG, KGNet significantly reduced training time and memory usage while maintaining comparable or improved accuracy. The KGNet source-code is available for further study

Related papers

• Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation [52.8352968531863]
  Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain.
  arXiv  Detail & Related papers  (2025-03-31T15:58:08Z)
• GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion [52.026016846945424]
  We propose a new method called GLTW, which encodes the structural information of KGs and merges it with Large Language Models. Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information. Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency.
  arXiv  Detail & Related papers  (2025-02-17T06:02:59Z)
• Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering [6.232269207752904]
  We present OntoSCPrompt, a novel Large Language Model (LLM)-based KGQA approach with a two-stage architecture. OntoSCPrompt first generates a SPARQL query structure (including SPARQL keywords such as SELECT, ASK, WHERE and placeholders for missing tokens) and then fills them with KG-specific information. We present several task-specific decoding strategies to ensure the correctness and executability of generated SPARQL queries in both stages.
  arXiv  Detail & Related papers  (2025-02-06T11:47:58Z)
• KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models [55.39134076436266]
  KG-CF is a framework tailored for ranking-based knowledge graph completion tasks. KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets.
  arXiv  Detail & Related papers  (2025-01-06T01:52:15Z)
• Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains [66.55612528039894]
  Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) We present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance.
  arXiv  Detail & Related papers  (2024-10-24T04:01:40Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)
• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling [5.460112864687281]
  This paper proposes KG-TOSA, an approach to automate the TOSG extraction for task-oriented HGNN training on a large Knowledge Graph (KG) KG-TOSA helps state-of-the-art HGNN methods reduce training time and memory usage by up to 70% while improving the model performance, e.g., accuracy and inference time.
  arXiv  Detail & Related papers  (2024-03-09T01:17:26Z)
• KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph [134.8631016845467]
  We propose an autonomous LLM-based agent framework, called KG-Agent. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG.
  arXiv  Detail & Related papers  (2024-02-17T02:07:49Z)
• KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models [18.20425100517317]
  We propose KG-GPT, a framework leveraging large language models for tasks employing knowledge graphs. KG-GPT comprises three steps: Sentence, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions. We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models.
  arXiv  Detail & Related papers  (2023-10-17T12:51:35Z)
• PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips [3.5923669681271257]
  PyGraft is a Python-based tool that generates customized, domain-agnostic schemas and KGs. We aim to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML) In ML, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks.
  arXiv  Detail & Related papers  (2023-09-07T13:00:09Z)
• A Universal Question-Answering Platform for Knowledge Graphs [7.2676028986202]
  We propose KGQAn, a universal QA system that does not need to be tailored to each target KG. KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time.
  arXiv  Detail & Related papers  (2023-03-01T15:35:32Z)
• Sequence-to-Sequence Knowledge Graph Completion and Question Answering [8.207403859762044]
  We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model. We achieve this by posing KG link prediction as a sequence-to-sequence task and exchange the triple scoring approach taken by prior KGE methods with autoregressive decoding.
  arXiv  Detail & Related papers  (2022-03-19T13:01:49Z)
• Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks [53.58077686470096]
  Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
  arXiv  Detail & Related papers  (2020-04-13T15:43:22Z)

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.