Issue
Transactions on Graph Data and Knowledge, Volume 1, Issue 1
TGDK, Volume 1, Issue 1
-
Part of:
Volume:
TGDK, Volume 1
Journal: Transactions on Graph Data and Knowledge (TGDK)
Special Issue
Special Issue on Trends in Graph Data and KnowledgeEditors
Publication Details
- published at: 2023-12-19
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/tgdk/tgdk1
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Issue
TGDK, Volume 1, Issue 1, Complete Issue
Abstract
TGDK, Volume 1, Issue 1, Complete Issue
Cite as
Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1: Special Issue on Trends in Graph Data and Knowledge, pp. 1-398, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{TGDK.1.1,
title = {{TGDK, Volume 1, Issue 1, Complete Issue}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1--398},
ISSN = {2942-7517},
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},
URN = {urn:nbn:de:0030-drops-194738},
doi = {10.4230/TGDK.1.1},
annote = {Keywords: TGDK, Volume 1, 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 1, Issue 1: Special Issue on Trends in Graph Data and Knowledge, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{TGDK.1.1.0,
title = {{Front Matter, Table of Contents, List of Authors}},
journal = {Transactions on Graph Data and Knowledge},
pages = {0:i--0:x},
ISSN = {2942-7517},
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.1.1.0},
URN = {urn:nbn:de:0030-drops-194746},
doi = {10.4230/TGDK.1.1.0},
annote = {Keywords: Front Matter, Table of Contents, List of Authors}
}
Document
Preface
Transactions on Graph Data and Knowledge
Abstract
Transactions on Graph Data and Knowledge (TGDK) is a new journal publishing peer-reviewed research on graph-based abstractions for data and knowledge, as well as the techniques, theories, applications and results that arise in this setting. TGDK is a community-run, Diamond Open Access journal, meaning that papers are published openly on the Web without fees for authors or readers. In this preface, we provide some brief remarks about the rationale and goals of the new journal, followed by an introduction to its inaugural issue, entitled "Trends in Graph Data and Knowledge", which collects together 12 diverse vision, position and survey papers on the types of research topics that exemplify the scope of this new journal.
Cite as
Aidan Hogan, Ian Horrocks, Andreas Hotho, and Lalana Kagal. Transactions on Graph Data and Knowledge. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 1:1-1:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{hogan_et_al:TGDK.1.1.1,
author = {Hogan, Aidan and Horrocks, Ian and Hotho, Andreas and Kagal, Lalana},
title = {{Transactions on Graph Data and Knowledge}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1:1--1:4},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.1},
URN = {urn:nbn:de:0030-drops-194757},
doi = {10.4230/TGDK.1.1.1},
annote = {Keywords: Graphs, Data, Knowledge}
}
Document
Position
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Abstract
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.
Cite as
Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux. Large Language Models and Knowledge Graphs: Opportunities and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 2:1-2:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{pan_et_al:TGDK.1.1.2,
author = {Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien},
title = {{Large Language Models and Knowledge Graphs: Opportunities and Challenges}},
journal = {Transactions on Graph Data and Knowledge},
pages = {2:1--2:38},
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.1.1.2},
URN = {urn:nbn:de:0030-drops-194766},
doi = {10.4230/TGDK.1.1.2},
annote = {Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}
}
Document
Vision
Knowledge Engineering Using Large Language Models
Abstract
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.
Cite as
Bradley P. Allen, Lise Stork, and Paul Groth. Knowledge Engineering Using Large Language Models. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{allen_et_al:TGDK.1.1.3,
author = {Allen, Bradley P. and Stork, Lise and Groth, Paul},
title = {{Knowledge Engineering Using Large Language Models}},
journal = {Transactions on Graph Data and Knowledge},
pages = {3:1--3:19},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.3},
URN = {urn:nbn:de:0030-drops-194777},
doi = {10.4230/TGDK.1.1.3},
annote = {Keywords: knowledge engineering, large language models}
}
Document
Survey
Knowledge Graph Embeddings: Open Challenges and Opportunities
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
Position
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
Abstract
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured.
The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery.
In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.
Cite as
Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, 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. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{chen_et_al:TGDK.1.1.5,
author = {Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jim\'{e}nez-Ruiz, Ernesto and L\'{o}pez, Vanessa and Monnin, Pierre and Pesquita, Catia and \v{S}koda, Petr and Tamma, Valentina},
title = {{Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities}},
journal = {Transactions on Graph Data and Knowledge},
pages = {5:1--5:33},
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.1.1.5},
URN = {urn:nbn:de:0030-drops-194791},
doi = {10.4230/TGDK.1.1.5},
annote = {Keywords: Knowledge graphs, Life science, Knowledge discovery, Explainable AI}
}
Document
Vision
Towards Ordinal Data Science
Abstract
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.
Cite as
Gerd Stumme, Dominik Dürrschnabel, and Tom Hanika. Towards Ordinal Data Science. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 6:1-6:39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{stumme_et_al:TGDK.1.1.6,
author = {Stumme, Gerd and D\"{u}rrschnabel, Dominik and Hanika, Tom},
title = {{Towards Ordinal Data Science}},
journal = {Transactions on Graph Data and Knowledge},
pages = {6:1--6:39},
ISSN = {2942-7517},
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.1.1.6},
URN = {urn:nbn:de:0030-drops-194801},
doi = {10.4230/TGDK.1.1.6},
annote = {Keywords: Order relation, data science, relational theory of measurement, metric learning, general algebra, lattices, factorization, approximations and heuristics, factor analysis, visualization, browsing, explainability}
}
Document
Survey
Rule Learning over Knowledge Graphs: A Review
Abstract
Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest, and a number of approaches to rule learning for knowledge graphs have been proposed. This survey aims to provide a review of approaches and a classification of state-of-the-art systems for learning first-order logic rules over knowledge graphs. A comparative analysis of various approaches to rule learning is conducted based on rule language biases, underlying methods, and evaluation metrics. The approaches we consider include inductive logic programming (ILP)-based, statistical path generalisation, and neuro-symbolic methods. Moreover, we highlight important and promising application scenarios of rule learning, such as rule-based knowledge graph completion, fact checking, and applications in other research areas.
Cite as
Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li. Rule Learning over Knowledge Graphs: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{wu_et_al:TGDK.1.1.7,
author = {Wu, Hong and Wang, Zhe and Wang, Kewen and Omran, Pouya Ghiasnezhad and Li, Jiangmeng},
title = {{Rule Learning over Knowledge Graphs: A Review}},
journal = {Transactions on Graph Data and Knowledge},
pages = {7:1--7:23},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.7},
URN = {urn:nbn:de:0030-drops-194813},
doi = {10.4230/TGDK.1.1.7},
annote = {Keywords: Rule learning, Knowledge graphs, Link prediction}
}
Document
Vision
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges
Abstract
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Cite as
Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{damato_et_al:TGDK.1.1.8,
author = {d'Amato, Claudia and Mahon, Louis and Monnin, Pierre and Stamou, Giorgos},
title = {{Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges}},
journal = {Transactions on Graph Data and Knowledge},
pages = {8:1--8:35},
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.1.1.8},
URN = {urn:nbn:de:0030-drops-194824},
doi = {10.4230/TGDK.1.1.8},
annote = {Keywords: Graph-based Learning, Knowledge Graph Embeddings, Large Language Models, Explainable AI, Knowledge Graph Completion \& Curation}
}
Document
Vision
Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination
Abstract
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.
Cite as
Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal. Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 9:1-9:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{ibanez_et_al:TGDK.1.1.9,
author = {Ib\'{a}\~{n}ez, Luis-Daniel and Domingue, John and Kirrane, Sabrina and Seneviratne, Oshani and Third, Aisling and Vidal, Maria-Esther},
title = {{Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination}},
journal = {Transactions on Graph Data and Knowledge},
pages = {9:1--9:32},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.9},
URN = {urn:nbn:de:0030-drops-194839},
doi = {10.4230/TGDK.1.1.9},
annote = {Keywords: Trust, Accountability, Autonomy, AI, Knowledge Graphs}
}
Document
Vision
Multilingual Knowledge Graphs and Low-Resource Languages: A Review
Abstract
There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multilingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages.
Cite as
Lucie-Aimée Kaffee, Russa Biswas, C. Maria Keet, Edlira Kalemi Vakaj, and Gerard de Melo. Multilingual Knowledge Graphs and Low-Resource Languages: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 10:1-10:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{kaffee_et_al:TGDK.1.1.10,
author = {Kaffee, Lucie-Aim\'{e}e and Biswas, Russa and Keet, C. Maria and Vakaj, Edlira Kalemi and de Melo, Gerard},
title = {{Multilingual Knowledge Graphs and Low-Resource Languages: A Review}},
journal = {Transactions on Graph Data and Knowledge},
pages = {10:1--10:19},
ISSN = {2942-7517},
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.1.1.10},
URN = {urn:nbn:de:0030-drops-194845},
doi = {10.4230/TGDK.1.1.10},
annote = {Keywords: knowledge graphs, multilingual, low-resource languages, review}
}
Document
Survey
How Does Knowledge Evolve in Open Knowledge Graphs?
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}
}
Document
Survey
Structural Summarization of Semantic Graphs Using Quotients
Abstract
Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph.
In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features. Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.
Cite as
Ansgar Scherp, David Richerby, Till Blume, Michael Cochez, and Jannik Rau. Structural Summarization of Semantic Graphs Using Quotients. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 12:1-12:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{scherp_et_al:TGDK.1.1.12,
author = {Scherp, Ansgar and Richerby, David and Blume, Till and Cochez, Michael and Rau, Jannik},
title = {{Structural Summarization of Semantic Graphs Using Quotients}},
journal = {Transactions on Graph Data and Knowledge},
pages = {12:1--12:25},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.12},
URN = {urn:nbn:de:0030-drops-194862},
doi = {10.4230/TGDK.1.1.12},
annote = {Keywords: graph summarization, quotients, stratified bisimulation}
}
Document
Vision
Autonomy in the Age of Knowledge Graphs: Vision and Challenges
Abstract
In this position paper, we propose that Knowledge Graphs (KGs) are one of the prime approaches to support the programming of autonomous software systems at the knowledge level. From this viewpoint, we survey how KGs can support different dimensions of autonomy in such systems: For example, the autonomy of systems with respect to their environment, or with respect to organisations; and we discuss related practical and research challenges. We emphasise that KGs need to be able to support systems of autonomous software agents that are themselves highly heterogeneous, which limits how these systems may use KGs. Furthermore, these heterogeneous software agents may populate highly dynamic environments, which implies that they require adaptive KGs. The scale of the envisioned systems - possibly stretching to the size of the Internet - highlights the maintainability of the underlying KGs that need to contain large-scale knowledge, which requires that KGs are maintained jointly by humans and machines. Furthermore, autonomous agents require procedural knowledge, and KGs should hence be explored more towards the provisioning of such knowledge to augment autonomous behaviour. Finally, we highlight the importance of modelling choices, including with respect to the selected abstraction level when modelling and with respect to the provisioning of more expressive constraint languages.
Cite as
Jean-Paul Calbimonte, Andrei Ciortea, Timotheus Kampik, Simon Mayer, Terry R. Payne, Valentina Tamma, and Antoine Zimmermann. Autonomy in the Age of Knowledge Graphs: Vision and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Article{calbimonte_et_al:TGDK.1.1.13,
author = {Calbimonte, Jean-Paul and Ciortea, Andrei and Kampik, Timotheus and Mayer, Simon and Payne, Terry R. and Tamma, Valentina and Zimmermann, Antoine},
title = {{Autonomy in the Age of Knowledge Graphs: Vision and Challenges}},
journal = {Transactions on Graph Data and Knowledge},
pages = {13:1--13:22},
ISSN = {2942-7517},
year = {2023},
volume = {1},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/TGDK.1.1.13},
URN = {urn:nbn:de:0030-drops-194872},
doi = {10.4230/TGDK.1.1.13},
annote = {Keywords: Knowledge graphs, Autonomous Systems}
}