Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data

• URL: http://arxiv.org/abs/2505.17498v1
• Date: Fri, 23 May 2025 05:51:00 GMT
• Title: Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data
• Authors: Marco Brandizi, Carlos Bobed, Luca Garulli, Arné de Klerk, Keywan Hassani-Pak,
• Abstract summary: Linked Data and labelled property graphs (LPG) are two data management approaches with complementary strengths and weaknesses.<n>We introduce rdf2pg, a framework for mapping RDF data to semantically equivalent LPG formats and data-bases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Linked Data and labelled property graphs (LPG) are two data management approaches with complementary strengths and weaknesses, making their integration beneficial for sharing datasets and supporting software ecosystems. In this paper, we introduce rdf2pg, an extensible framework for mapping RDF data to semantically equivalent LPG formats and data-bases. Utilising this framework, we perform a comparative analysis of three popular graph databases - Virtuoso, Neo4j, and ArcadeDB - and the well-known graph query languages SPARQL, Cypher, and Gremlin. Our qualitative and quantitative as-sessments underline the strengths and limitations of these graph database technologies. Additionally, we highlight the potential of rdf2pg as a versatile tool for enabling polyglot access to knowledge graphs, aligning with established standards of Linked Data and the Semantic Web.

Related papers

• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Common Foundations for SHACL, ShEx, and PG-Schema [0.0]
  There is a need to describe the schema of such graphs.<n>Both the Semantic Web and the database community have independently developed graph schema languages: SHA, ShEx, and PG-CL.<n>Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences.<n>We employ a uniform framework to facilitate a comprehensive comparison between the languages and identify a common set of functionalities.
  arXiv  Detail & Related papers  (2025-02-03T12:17:25Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information.<n>Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information.<n>Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data [42.18348019901044]
  GT2Vec is a framework that leverages Large Language Models to jointly encode text and graph data.<n>Unlike prior work, we also introduce contrastive learning to align the graph and text spaces more effectively.
  arXiv  Detail & Related papers  (2024-10-15T03:40:20Z)
• TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs [14.437863803271808]
  Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections. Existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. To address this gap, we introduce Textual-Edge Graphs datasets featuring rich textual descriptions on nodes and edges.
  arXiv  Detail & Related papers  (2024-06-14T06:22:47Z)
• Federated Neural Graph Databases [53.03085605769093]
  We propose Federated Neural Graph Database (FedNGDB), a novel framework that enables reasoning over multi-source graph-based data while preserving privacy. Unlike existing methods, FedNGDB can handle complex graph structures and relationships, making it suitable for various downstream tasks.
  arXiv  Detail & Related papers  (2024-02-22T14:57:44Z)
• Large Language Models on Graphs: A Comprehensive Survey [77.16803297418201]
  We provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-attributed graphs, and text-paired graphs. We discuss the real-world applications of such methods and summarize open-source codes and benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-05T14:14:27Z)
• GraphGPT: Graph Instruction Tuning for Large Language Models [27.036935149004726]
  Graph Neural Networks (GNNs) have evolved to understand graph structures. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Our research tackles this by advancing graph model generalization in zero-shot learning environments.
  arXiv  Detail & Related papers  (2023-10-19T06:17:46Z)
• Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
  Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories.
  arXiv  Detail & Related papers  (2023-10-09T07:59:34Z)
• Neural Graph Databases [14.711031610812967]
  We show how to seamlessly combine nearly any graph neural network (GNN) model with the computational capabilities of graph databases (GDBs) We develop LPG2vec, an encoder that transforms an arbitrary LPG dataset into a representation that can be directly used with a broad class of GNNs. In our evaluation, we show that the rich information represented as LPG labels and properties is properly preserved by LPG2vec.
  arXiv  Detail & Related papers  (2022-09-20T14:06:44Z)
• Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases [9.021529689292985]
  We present Expressive Reasoning Graph Store (ERGS) ERGS is a graph store built on top of JanusGraph that also allows storing and querying of RDF datasets. We describe how RDF data can be translated into a Property Graph representation and then describe a query translation module that converts SPARQL queries into a series of Gremlins.
  arXiv  Detail & Related papers  (2022-09-13T09:07:50Z)
• Open Graph Benchmark: Datasets for Machine Learning on Graphs [86.96887552203479]
  We present the Open Graph Benchmark (OGB) to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics.
  arXiv  Detail & Related papers  (2020-05-02T03:09:50Z)

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.