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Transactions on Graph Data and Knowledge, Volume 2, Issue 1
TGDK, Volume 2, Issue 1
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TGDK, Volume 2
Journal: Transactions on Graph Data and Knowledge (TGDK)
Publication Details
- published at: 2024-05-03
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Document
Complete Issue
TGDK, Volume 2, Issue 1, Complete Issue
Abstract
TGDK, Volume 2, Issue 1, Complete Issue
Cite as
Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1: Special Issue on Trends in Graph Data and Knowledge - Part 2, pp. 1-180, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{TGDK.2.1,
title = {{TGDK, Volume 2, Issue 1, Complete Issue}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1--180},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1},
URN = {urn:nbn:de:0030-drops-198568},
doi = {10.4230/TGDK.2.1},
annote = {Keywords: TGDK, Volume 2, 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 2, Issue 1: Special Issue on Trends in Graph Data and Knowledge - Part 2, pp. 0:i-0:viii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{TGDK.2.1.0,
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 = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.0},
URN = {urn:nbn:de:0030-drops-198578},
doi = {10.4230/TGDK.2.1.0},
annote = {Keywords: Front Matter, Table of Contents, List of Authors}
}
Document
Survey
Towards Representing Processes and Reasoning with Process Descriptions on the Web
Abstract
We work towards a vocabulary to represent processes and temporal logic specifications as graph-structured data. Different fields use incompatible terminologies for describing essentially the same process-related concepts. In addition, processes can be represented from different perspectives and levels of abstraction: both state-centric and event-centric perspectives offer distinct insights into the underlying processes. In this work, we strive to unify the representation of processes and related concepts by leveraging the power of knowledge graphs. We survey approaches to representing processes and reasoning with process descriptions from different fields and provide a selection of scenarios to help inform the scope of a unified representation of processes. We focus on processes that can be executed and observed via web interfaces. We propose to provide a representation designed to combine state-centric and event-centric perspectives while incorporating temporal querying and reasoning capabilities on temporal logic specifications. A standardised vocabulary and representation for processes and temporal specifications would contribute towards bridging the gap between the terminologies from different fields and fostering the broader application of methods involving temporal logics, such as formal verification and program synthesis.
Cite as
Andreas Harth, Tobias Käfer, Anisa Rula, Jean-Paul Calbimonte, Eduard Kamburjan, and Martin Giese. Towards Representing Processes and Reasoning with Process Descriptions on the Web. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 1:1-1:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{harth_et_al:TGDK.2.1.1,
author = {Harth, Andreas and K\"{a}fer, Tobias and Rula, Anisa and Calbimonte, Jean-Paul and Kamburjan, Eduard and Giese, Martin},
title = {{Towards Representing Processes and Reasoning with Process Descriptions on the Web}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1:1--1:32},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.1},
URN = {urn:nbn:de:0030-drops-198583},
doi = {10.4230/TGDK.2.1.1},
annote = {Keywords: Process modelling, Process ontology, Temporal logic, Web services}
}
Document
Position
Grounding Stream Reasoning Research
Abstract
In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic.
In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream.
This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
Cite as
Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer. Grounding Stream Reasoning Research. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 2:1-2:47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{bonte_et_al:TGDK.2.1.2,
author = {Bonte, Pieter and Calbimonte, Jean-Paul and de Leng, Daniel and Dell'Aglio, Daniele and Della Valle, Emanuele and Eiter, Thomas and Giannini, Federico and Heintz, Fredrik and Schekotihin, Konstantin and Le-Phuoc, Danh and Mileo, Alessandra and Schneider, Patrik and Tommasini, Riccardo and Urbani, Jacopo and Ziffer, Giacomo},
title = {{Grounding Stream Reasoning Research}},
journal = {Transactions on Graph Data and Knowledge},
pages = {2:1--2:47},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.2},
URN = {urn:nbn:de:0030-drops-198597},
doi = {10.4230/TGDK.2.1.2},
annote = {Keywords: Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases}
}
Document
Survey
Semantic Web: Past, Present, and Future
Abstract
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning.
In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called "Semantic Web Layer Cake" with an update of recent concepts that include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We conclude with an outlook on the future directions of the Semantic Web.
This is a living document. If you like to contribute, please contact the first author and visit: https://github.com/ascherp/semantic-web-primer
Cite as
Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal. Semantic Web: Past, Present, and Future. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 3:1-3:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{scherp_et_al:TGDK.2.1.3,
author = {Scherp, Ansgar and Groener, Gerd and \v{S}koda, Petr and Hose, Katja and Vidal, Maria-Esther},
title = {{Semantic Web: Past, Present, and Future}},
journal = {Transactions on Graph Data and Knowledge},
pages = {3:1--3:37},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.3},
URN = {urn:nbn:de:0030-drops-198607},
doi = {10.4230/TGDK.2.1.3},
annote = {Keywords: Linked Open Data, Semantic Web Graphs, Knowledge Graphs}
}
Document
Survey
Logics for Conceptual Data Modelling: A Review
Abstract
Information modelling for databases and object-oriented information systems avails of conceptual data modelling languages such as EER and UML Class Diagrams. Many attempts exist to add logical rigour to them, for various reasons and with disparate strengths. In this paper we aim to provide a structured overview of the many efforts. We focus on aims, approaches to the formalisation, including key dimensions of choice points, popular logics used, and the main relevant reasoning services. We close with current challenges and research directions.
Cite as
Pablo R. Fillottrani and C. Maria Keet. Logics for Conceptual Data Modelling: A Review. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 4:1-4:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{fillottrani_et_al:TGDK.2.1.4,
author = {Fillottrani, Pablo R. and Keet, C. Maria},
title = {{Logics for Conceptual Data Modelling: A Review}},
journal = {Transactions on Graph Data and Knowledge},
pages = {4:1--4:30},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.4},
URN = {urn:nbn:de:0030-drops-198616},
doi = {10.4230/TGDK.2.1.4},
annote = {Keywords: Conceptual Data Modelling, EER, UML, Description Logics, OWL}
}
Document
Position
Standardizing Knowledge Engineering Practices with a Reference Architecture
Abstract
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best, however, this direction has not been explored to date. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, consisting of scope definition, selection of information sources, architectural analysis, synthesis of an architecture based on the information source analysis, evaluation through instantiation, and, ultimately, instantiation into a concrete software architecture. We provide an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. As the remaining steps of design, evaluation, and instantiation of the architecture are largely use-case specific, we provide a detailed description of their procedures and point to relevant examples. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.
Cite as
Bradley P. Allen and Filip Ilievski. Standardizing Knowledge Engineering Practices with a Reference Architecture. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{allen_et_al:TGDK.2.1.5,
author = {Allen, Bradley P. and Ilievski, Filip},
title = {{Standardizing Knowledge Engineering Practices with a Reference Architecture}},
journal = {Transactions on Graph Data and Knowledge},
pages = {5:1--5:23},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.5},
URN = {urn:nbn:de:0030-drops-198623},
doi = {10.4230/TGDK.2.1.5},
annote = {Keywords: knowledge engineering, knowledge graphs, quality attributes, software architectures, sociotechnical systems}
}