Search Results

Documents authored by Dumbrava, Stefania

Document
Preface
DOI: 10.4230/TGDK.4.2.1
Data Management for (Knowledge) Graphs

Authors: Stefania Dumbrava, George Fletcher, Matteo Lissandrini, and Riccardo Tommasini

Published in: TGDK, Volume 4, Issue 2 (2026): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2

Abstract
In this Special Issue of Transactions on Graph Data and Knowledge, entitled "Data Management for (Knowledge) Graphs", we present eight articles: one position paper, four research papers, and three resource papers. These span graph data querying, transformation, integration, evolution, and evaluation. Their applications include legislative knowledge management, geospatial data access, and knowledge graph construction from heterogeneous sources with large language models.
Cite as

Stefania Dumbrava, George Fletcher, Matteo Lissandrini, and Riccardo Tommasini. Data Management for (Knowledge) Graphs. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{dumbrava_et_al:TGDK.4.2.1,
  author =	{Dumbrava, Stefania and Fletcher, George and Lissandrini, Matteo and Tommasini, Riccardo},
  title =	{{Data Management for (Knowledge) Graphs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:2},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.1},
  URN =		{urn:nbn:de:0030-drops-275828},
  doi =		{10.4230/TGDK.4.2.1},
  annote =	{Keywords: graph data, property graphs, knowledge graphs, RDF, data management}
}
Document
Position
DOI: 10.4230/TGDK.4.2.2
How Human-Centric Are Our Graph Data Abstractions?

Authors: Angela Bonifati, Anastasia Dimou, Stefania Dumbrava, George Fletcher, Katja Hose, George Konstantinidis, Jose Emilio Labra Gayo, Wim Martens, Nina Pardal, Liat Peterfreund, Katherine Thornton, Maria-Esther Vidal, and Hannes Voigt

Published in: TGDK, Volume 4, Issue 2 (2026): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2

Abstract
Graph data abstractions are often assumed to be intuitive, but experience shows that they are not equally understandable or usable in practice. In this vision and challenges paper, we examine the human-centricity of contemporary graph data abstractions through four lenses: researchability, usability, teachability, and societal impact. Drawing on diverse real-world use cases, ranging from clinical data and collaborative knowledge bases to biological and pangenomic graphs, we distill insights from database research, human-computer interaction, and education. Based on this analysis, we identify open research challenges that must be addressed to make graph abstractions easier to study, use, learn, and reason about.
Cite as

Angela Bonifati, Anastasia Dimou, Stefania Dumbrava, George Fletcher, Katja Hose, George Konstantinidis, Jose Emilio Labra Gayo, Wim Martens, Nina Pardal, Liat Peterfreund, Katherine Thornton, Maria-Esther Vidal, and Hannes Voigt. How Human-Centric Are Our Graph Data Abstractions?. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{bonifati_et_al:TGDK.4.2.2,
  author =	{Bonifati, Angela and Dimou, Anastasia and Dumbrava, Stefania and Fletcher, George and Hose, Katja and Konstantinidis, George and Gayo, Jose Emilio Labra and Martens, Wim and Pardal, Nina and Peterfreund, Liat and Thornton, Katherine and Vidal, Maria-Esther and Voigt, Hannes},
  title =	{{How Human-Centric Are Our Graph Data Abstractions?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:31},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.2},
  URN =		{urn:nbn:de:0030-drops-275837},
  doi =		{10.4230/TGDK.4.2.2},
  annote =	{Keywords: graph data abstractions, property graphs, RDF, human-centric data management, usability, computing education}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.4
Knowledge Graph Embeddings: Open Challenges and Opportunities

Authors: Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.
Cite as

Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo. Knowledge Graph Embeddings: Open Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 4:1-4:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@Article{biswas_et_al:TGDK.1.1.4,
  author =	{Biswas, Russa and Kaffee, Lucie-Aim\'{e}e and Cochez, Michael and Dumbrava, Stefania and Jendal, Theis E. and Lissandrini, Matteo and Lopez, Vanessa and Menc{\'\i}a, Eneldo Loza and Paulheim, Heiko and Sack, Harald and Vakaj, Edlira Kalemi and de Melo, Gerard},
  title =	{{Knowledge Graph Embeddings: Open Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:32},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.4},
  URN =		{urn:nbn:de:0030-drops-194783},
  doi =		{10.4230/TGDK.1.1.4},
  annote =	{Keywords: Knowledge Graphs, KG embeddings, Link prediction, KG applications}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.11
How Does Knowledge Evolve in Open Knowledge Graphs?

Authors: Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.
Cite as

Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@Article{polleres_et_al:TGDK.1.1.11,
  author =	{Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jim\'{e}nez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
  title =	{{How Does Knowledge Evolve in Open Knowledge Graphs?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{11:1--11:59},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},
  URN =		{urn:nbn:de:0030-drops-194855},
  doi =		{10.4230/TGDK.1.1.11},
  annote =	{Keywords: KG evolution, temporal KG, versioned KG, dynamic KG}
}

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact