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Transactions on Graph Data and Knowledge, Volume 3, Issue 1
TGDK, Volume 3, Issue 1
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TGDK, Volume 3
Journal: Transactions on Graph Data and Knowledge (TGDK)
Publication Details
- published at: 2025-04-11
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Document
Complete Issue
TGDK, Volume 3, Issue 1, Complete Issue
Abstract
TGDK, Volume 3, Issue 1, Complete Issue
Cite as
Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 1-140, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{TGDK.3.1,
title = {{TGDK, Volume 3, Issue 1, Complete Issue}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1--140},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1},
URN = {urn:nbn:de:0030-drops-238256},
doi = {10.4230/TGDK.3.1},
annote = {Keywords: TGDK, Volume 3, Issue 1, Complete Issue}
}
Document
Front Matter
Front Matter, Table of Contents, List of Authors
Abstract
Front Matter, Table of Contents, List of Authors
Cite as
Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 0:i-0:viii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{TGDK.3.1.0,
title = {{Front Matter, Table of Contents, List of Authors}},
journal = {Transactions on Graph Data and Knowledge},
pages = {0:i--0:viii},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.0},
URN = {urn:nbn:de:0030-drops-230160},
doi = {10.4230/TGDK.3.1.0},
annote = {Keywords: Front Matter, Table of Contents, List of Authors}
}
Document
Resource
KG2Tables: A Domain-Specific Tabular Data Generator to Evaluate Semantic Table Interpretation Systems
Abstract
Tabular data, often in the form of CSV files, plays a pivotal role in data analytics pipelines. Understanding this data semantically, known as Semantic
Table Interpretation (STI), is crucial but poses challenges due to several factors such as the ambiguity of labels. As a result, STI has gained increasing attention from the community in the past few years. Evaluating STI systems requires well-established benchmarks. Most of the existing large-scale benchmarks are derived from general domain sources and focus on ambiguity, while domain-specific benchmarks are relatively small in size. This paper introduces KG2Tables, a framework that can construct domain-specific large-scale benchmarks from a Knowledge Graph (KG). KG2Tables leverages the internal hierarchy of the relevant KG concepts and their properties. As a proof of concept, we have built large datasets in the food, biodiversity, and biomedical domains. The resulting datasets, tFood, tBiomed, and tBiodiv, have been made available for the public in the ISWC SemTab challenge (2023 and 2024 editions). We include the evaluation results of top-performing STI systems using tFood Such results underscore its potential as a robust evaluation benchmark for challenging STI systems. We demonstrate the data quality level using a sample-based approach for the generated benchmarks including, for example, realistic tables assessment. Nevertheless, we provide an extensive discussion of KG2Tables explaining how it could be used to create other benchmarks from any domain of interest and including its key features and limitations with suggestions to overcome them.
Cite as
Nora Abdelmageed, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, and Birgitta König-Ries. KG2Tables: A Domain-Specific Tabular Data Generator to Evaluate Semantic Table Interpretation Systems. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 1:1-1:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{abdelmageed_et_al:TGDK.3.1.1,
author = {Abdelmageed, Nora and Jim\'{e}nez-Ruiz, Ernesto and Hassanzadeh, Oktie and K\"{o}nig-Ries, Birgitta},
title = {{KG2Tables: A Domain-Specific Tabular Data Generator to Evaluate Semantic Table Interpretation Systems}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1:1--1:28},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.1},
URN = {urn:nbn:de:0030-drops-230104},
doi = {10.4230/TGDK.3.1.1},
annote = {Keywords: Semantic Table Interpretation (STI), Knowledge Graph (KG), STI Benchmark, Food, Biodiversity, Biomedical}
}
Document
Research
Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs
Abstract
We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities.
Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project.
Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.
Cite as
Elisavet Koutsiana, Ioannis Reklos, Kholoud Saad Alghamdi, Nitisha Jain, Albert Meroño-Peñuela, and Elena Simperl. Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 2:1-2:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{koutsiana_et_al:TGDK.3.1.2,
author = {Koutsiana, Elisavet and Reklos, Ioannis and Alghamdi, Kholoud Saad and Jain, Nitisha and Mero\~{n}o-Pe\~{n}uela, Albert and Simperl, Elena},
title = {{Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs}},
journal = {Transactions on Graph Data and Knowledge},
pages = {2:1--2:27},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.2},
URN = {urn:nbn:de:0030-drops-230114},
doi = {10.4230/TGDK.3.1.2},
annote = {Keywords: collaborative knowledge graph, network analysis, graph embeddings, text embeddings}
}
Document
Survey
Uncertainty Management in the Construction of Knowledge Graphs: A Survey
Abstract
Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in data representation and their numerous applications, e.g., vocabulary sharing, Q&A or recommendation systems. To build a KG, it is a common practice to rely on automatic methods for extracting knowledge from various heterogeneous sources. However, in a noisy and uncertain world, knowledge may not be reliable and conflicts between data sources may occur. Integrating unreliable data would directly impact the use of the KG, therefore such conflicts must be resolved. This could be done manually by selecting the best data to integrate. This first approach is highly accurate, but costly and time-consuming. That is why recent efforts focus on automatic approaches, which represent a challenging task since it requires handling the uncertainty of extracted knowledge throughout its integration into the KG. We survey state-of-the-art approaches in this direction and present constructions of both open and enterprise KGs. We then describe different knowledge extraction methods and discuss downstream tasks after knowledge acquisition, including KG completion using embedding models, knowledge alignment, and knowledge fusion in order to address the problem of knowledge uncertainty in KG construction. We conclude with a discussion on the remaining challenges and perspectives when constructing a KG taking into account uncertainty.
Cite as
Lucas Jarnac, Yoan Chabot, and Miguel Couceiro. Uncertainty Management in the Construction of Knowledge Graphs: A Survey. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 3:1-3:48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{jarnac_et_al:TGDK.3.1.3,
author = {Jarnac, Lucas and Chabot, Yoan and Couceiro, Miguel},
title = {{Uncertainty Management in the Construction of Knowledge Graphs: A Survey}},
journal = {Transactions on Graph Data and Knowledge},
pages = {3:1--3:48},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.3},
URN = {urn:nbn:de:0030-drops-233733},
doi = {10.4230/TGDK.3.1.3},
annote = {Keywords: Knowledge reconciliation, Uncertainty, Heterogeneous sources, Knowledge graph construction}
}
Document
Research
CoaKG: A Contextualized Knowledge Graph Approach for Exploratory Search and Decision Making
Abstract
In decision-making scenarios, an information need arises due to a knowledge gap when a decision-maker needs more knowledge to make a decision. Users may take the initiative to acquire knowledge to fill this gap through exploratory search approaches using Knowledge Graphs (KGs) as information sources, but their queries can be incomplete, inaccurate, and ambiguous. Although KGs have great potential for exploratory search, they are incomplete by nature. Besides, for both Crowd-sourced KGs and KGs constructed by integrating several different information sources of varying quality to be effectively consumed, there is a need for a Trust Layer. Our research aims to enrich and allow querying KGs to support context-aware exploration in decision-making scenarios. We propose a layered architecture for Context Augmented Knowledge Graphs-based Decision Support Systems with a Knowledge Layer that operates under a Dual Open World Assumption (DOWA). Under DOWA, the evaluation of the truthfulness of the information obtained from KGs depends on the context of its claims and the tasks carried out or intended (purpose). The Knowledge Layer comprises a Context Augmented KG (CoaKG) and a CoaKG Query Engine. The CoaKG contains contextual mappings to identify explicit context and rules to infer implicit context. The CoaKG Query Engine is designed as a query-answering approach that retrieves all contextualized answers from the CoaKG. A Proof of Concept (PoC) based on Wikidata was developed to evaluate the effectiveness of the Knowledge Layer.
Cite as
Veronica dos Santos, Daniel Schwabe, Altigran Soares da Silva, and Sérgio Lifschitz. CoaKG: A Contextualized Knowledge Graph Approach for Exploratory Search and Decision Making. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 4:1-4:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{dossantos_et_al:TGDK.3.1.4,
author = {dos Santos, Veronica and Schwabe, Daniel and da Silva, Altigran Soares and Lifschitz, S\'{e}rgio},
title = {{CoaKG: A Contextualized Knowledge Graph Approach for Exploratory Search and Decision Making}},
journal = {Transactions on Graph Data and Knowledge},
pages = {4:1--4:27},
ISSN = {2942-7517},
year = {2025},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.4},
URN = {urn:nbn:de:0030-drops-236685},
doi = {10.4230/TGDK.3.1.4},
annote = {Keywords: Knowledge Graphs, Context Search, Decision Support}
}