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Transactions on Graph Data and Knowledge, Volume 4, Issue 3

TGDK, Volume 4, Issue 3

Publication Details

  • published at: 2026-09-30
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
Survey
DOI: 10.4230/TGDK.4.3.1
A Systematic Survey on Synthetic Knowledge Graph Generators

Authors: Ana Alexandra Morim da Silva, Michael Röder, and Axel-Cyrille Ngonga Ngomo

Abstract
Knowledge graphs are the backbone of an increasing number of data-driven software solutions, where performance, i.e., runtime and accuracy, depends critically on the data they process. Consequently, synthetic knowledge graph generators become essential tools for reliable evaluation and future performance prediction of knowledge graph-driven solutions. These generators address critical challenges, including the exponential growth of knowledge graphs, the difficulty in acquiring real-world domain knowledge graphs, and the need for realistic data to predict future behaviour under realistic workloads. Generators overcome these challenges by producing scalable and controllable knowledge graph structures. Knowledge graphs at various scales enable the testing of existing solutions on larger-than-current graphs, supporting future-proof solutions. Controllable structures, in turn, support the generation of knowledge graphs in data-scarce domains and with realistic properties. This survey comprehensively analyzes the current landscape of synthetic knowledge graph generators, including evaluation tasks and metrics. We catalog 38 generators published between 2014 and 2025 based on their methodology, generation requirements, and the type of generated graph. We also survey the evaluation tasks and technical details reported for these generators, providing a reference for both practitioners looking for a generator and researchers designing new ones. We conclude with open challenges and promising research directions.
Cite as

Ana Alexandra Morim da Silva, Michael Röder, and Axel-Cyrille Ngonga Ngomo. A Systematic Survey on Synthetic Knowledge Graph Generators. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 3, pp. 1:1-1:50, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{morimdasilva_et_al:TGDK.4.3.1,
  author =	{Morim da Silva, Ana Alexandra and R\"{o}der, Michael and Ngonga Ngomo, Axel-Cyrille},
  title =	{{A Systematic Survey on Synthetic Knowledge Graph Generators}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:50},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.3.1},
  URN =		{urn:nbn:de:0030-drops-280864},
  doi =		{10.4230/TGDK.4.3.1},
  annote =	{Keywords: Survey, Synthetic Knowledge Graph Generation, Knowledge Graph, Synthetic Data, Graph Generation}
}
Document
Research
DOI: 10.4230/TGDK.4.3.2
AutoRML: Automated Declarative RDF Generation Leveraging Semantic Table Annotation

Authors: Ioannis Dasoulas, Ali Elhalawati, and Anastasia Dimou

Abstract
Knowledge graph (KG) construction from heterogeneous data is a complex process that requires good understanding of the data to semantically annotate entities and their relations, and define the terms of the KG based on these entities and relations. Due to this complexity, KG construction remains primarily manual. On the one hand, semantic annotation systems focus on entity and relation disambiguation, but ultimately they do not construct a KG. On the other hand, declarative systems which typically construct the KGs, assume that the semantic annotations are already available. However, the two types of systems have not yet been effectively integrated. In this paper, we propose a formal method that integrates semantic annotation and declarative systems for automating end-to-end KG construction through automated declarative mappings generation. We validate our approach by creating AutoRML, a system that can leverage different semantic annotation frameworks to annotate tabular data and declaratively construct KGs. Evaluations demonstrate that AutoRML can construct KGs identical to manually constructed ones when the same target knowledge base is used as reference. AutoRML supports semi-automatic KG construction when semantic annotations are insufficient for full automation, generating human-friendly declarative mappings that can be refined by experts. We showcase AutoRML’s performance with diverse datasets, showing its potential in automating data integration workflows.
Cite as

Ioannis Dasoulas, Ali Elhalawati, and Anastasia Dimou. AutoRML: Automated Declarative RDF Generation Leveraging Semantic Table Annotation. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 3, pp. 2:1-2:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{dasoulas_et_al:TGDK.4.3.2,
  author =	{Dasoulas, Ioannis and Elhalawati, Ali and Dimou, Anastasia},
  title =	{{AutoRML: Automated Declarative RDF Generation Leveraging Semantic Table Annotation}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:34},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.3.2},
  URN =		{urn:nbn:de:0030-drops-280874},
  doi =		{10.4230/TGDK.4.3.2},
  annote =	{Keywords: Knowledge Graphs, Mapping Languages, RML, Semantic Table Annotation, Entity Linking, Property Linking}
}
Document
Research
DOI: 10.4230/TGDK.4.3.3
A Hybrid Vector-Symbolic Data Model for Multimodal Knowledge Graphs

Authors: Florian Ruosch and Luca Rossetto

Abstract
In this article, we address the architectural and representational “schism” between symbolic knowledge graphs and sub-symbolic multimodal data. By formalizing the MediaGraph Data Model, we demonstrate that high-dimensional vectors and multimedia segments can be integrated as first-class citizens within a unified graph structure. This shift from treating vectors as opaque literals to queryable nodes enables “early-binding” query execution, allowing the SPARQL engine to jointly optimize over both structural relationships and perceptual similarity. Our implementation, MeGraS, provides a scalable engine that mitigates the long-standing N+1 query problem through batched execution. The experimental evaluation on both real-world lifelogging data and a synthetic dataset of 10⁷ triples confirms that our hybrid approach maintains high throughput and retrieval efficiency, even as the complexity of multimodal queries increases. Furthermore, the integration of the Unified Multimedia Segmentation Model ensures that media granularity is preserved, making complex spatiotemporal and content-aware querying possible within a single query framework. Ultimately, MeGraS provides a foundational infrastructure for the next generation of multimodal intelligent systems. By unifying the symbolic and sub-symbolic domains, we provide a pathway toward more expressive and efficient retrieval strategies that can keep pace with the explosion of heterogeneous digital media.
Cite as

Florian Ruosch and Luca Rossetto. A Hybrid Vector-Symbolic Data Model for Multimodal Knowledge Graphs. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 3, pp. 3:1-3:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{ruosch_et_al:TGDK.4.3.3,
  author =	{Ruosch, Florian and Rossetto, Luca},
  title =	{{A Hybrid Vector-Symbolic Data Model for Multimodal Knowledge Graphs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:28},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.3.3},
  URN =		{urn:nbn:de:0030-drops-280880},
  doi =		{10.4230/TGDK.4.3.3},
  annote =	{Keywords: Multimodal Knowledge Graphs, Graph Store, Multimodal Media Segmentation}
}

Filters

  • Authors
  • D
  • Dasoulas, Ioannis
  • Dimou, Anastasia
  • E
  • Elhalawati, Ali
  • M
  • Morim da Silva, Ana Alexandra
  • N
  • Ngonga Ngomo, Axel-Cyrille
  • R
  • Röder, Michael
  • Rossetto, Luca
  • Ruosch, Florian

  • Subjects
  • General and reference
  • General and reference → Surveys and overviews
  • Information systems
  • Information systems → Extraction, transformation and loading
  • Information systems → Graph-based database models
  • Information systems → Multimedia and multimodal retrieval
  • Information systems → Resource Description Framework (RDF)
  • Information systems → Users and interactive retrieval
  • Theory of computation
  • Theory of computation → Graph algorithms analysis

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