3 Search Results for "Barbosa, Diana"

Document
Research
DOI: 10.4230/TGDK.4.1.1
On the Computational Cost of Knowledge Graph Embeddings

Authors: Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann

Published in: TGDK, Volume 4, Issue 1 (2026). Transactions on Graph Data and Knowledge, Volume 4, Issue 1

Abstract
Over a decade, numerous Knowledge Graph Embedding (KGE) models have been designed and evaluated on reference datasets, always with increasing performance. In this paper, we re-evaluate these models with respect to their computational efficiency during training, by estimating the computational cost of the procedure expressed in floating-point operations. We design a cost model based on analytical expressions and apply it on a collection of 20 KGE models, representative of the state-of-the-art. We show that dimensionality or parameter efficiency, used in the literature to compare models with each other, are not suitable to evaluate the true cost of models. Through fixed-budget experiments, a novel approach to evaluate KGE models based on cost estimates, we re-assess the relative performance of model families compared to the state-of-the-art. Bilinear models such as ComplEx underperform with a low computational budget while hyperbolic linear models appear to offer no particular benefit compared to simpler Euclidian models, especially the MuRE model. Neural models, such as ConvE or CompGCN, achieve reasonable performance in the literature but their high computational cost appears unnecessary when compared with other models. The trade-off between efficiency and expressivity of both linear and neural models is to be further explored.
Cite as

Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann. On the Computational Cost of Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 1:1-1:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{charpenay_et_al:TGDK.4.1.1,
  author =	{Charpenay, Victor and Zoubeirou A Mayaki, Mansour and Zimmermann, Antoine},
  title =	{{On the Computational Cost of Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:30},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.1.1},
  URN =		{urn:nbn:de:0030-drops-256863},
  doi =		{10.4230/TGDK.4.1.1},
  annote =	{Keywords: Knowledge Graph Embedding, Parameter Efficiency, Computational Budget, Green AI}
}
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)

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@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}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2020.27
Improving Game-Based Learning Experience Through Game Appropriation

Authors: Salete Teixeira, Diana Barbosa, Cristiana Araújo, and Pedro R. Henriques

Published in: OASIcs, Volume 81, First International Computer Programming Education Conference (ICPEC 2020)

Abstract
Computational Thinking is an essential concept in this technological age. Several countries have included this subject as part of their educational program, and many others intend to do it. However, this is not a regular subject like maths or history; it needs more training (to increase the capabilities/skills) than studying and memorizing concepts. So it comes clear that the introduction of Computational Thinking to students requires the choice of the most suitable learning resources. Game-Based Learning was proven to be an effective teaching method. Therefore, we elected games as our learning resources. Nonetheless, we believe that the learning experience and motivation of students when playing games can be improved by choosing the most suitable game for each student. So, this paper focuses on the adaptation of Game-Based Learning to each student to develop Computational Thinking. We will argue that this adaptation can be done in a computer supported systematic way. To make that possible, on one hand, it will be necessary to classify games - an original ontology was used for that. On the other hand, it is crucial to establish the students' profile, having into consideration sociodemographic factors, personality, level of education, among others. Then, resorting to a similarity evaluation process it is feasible to choose the games that best fit the players, augmenting the effectiveness of the learning experience. We intend to start applying our approach - training Computational Thinking - to young students, since the first scholar years. However, we are also considering its application to adults starting programming studies.
Cite as

Salete Teixeira, Diana Barbosa, Cristiana Araújo, and Pedro R. Henriques. Improving Game-Based Learning Experience Through Game Appropriation. In First International Computer Programming Education Conference (ICPEC 2020). Open Access Series in Informatics (OASIcs), Volume 81, pp. 27:1-27:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{teixeira_et_al:OASIcs.ICPEC.2020.27,
  author =	{Teixeira, Salete and Barbosa, Diana and Ara\'{u}jo, Cristiana and Henriques, Pedro R.},
  title =	{{Improving Game-Based Learning Experience Through Game Appropriation}},
  booktitle =	{First International Computer Programming Education Conference (ICPEC 2020)},
  pages =	{27:1--27:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-153-5},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{81},
  editor =	{Queir\'{o}s, Ricardo and Portela, Filipe and Pinto, M\'{a}rio and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2020.27},
  URN =		{urn:nbn:de:0030-drops-123144},
  doi =		{10.4230/OASIcs.ICPEC.2020.27},
  annote =	{Keywords: Computational Thinking, Computing Education, Game-Based Learning, Game Types, Ontology, Student Profile, Adult Learning}
}
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