Issue
Transactions on Graph Data and Knowledge, Volume 2, Issue 3
TGDK, Volume 2, Issue 3
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TGDK, Volume 2
Journal: Transactions on Graph Data and Knowledge (TGDK)
Publication Details
- published at: 2024-12-18
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Document
Complete Issue
TGDK, Volume 2, Issue 3, Complete Issue
Abstract
TGDK, Volume 2, Issue 3, Complete Issue
Cite as
Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 3, pp. 1-72, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{TGDK.2.3,
title = {{TGDK, Volume 2, Issue 3, Complete Issue}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1--72},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.3},
URN = {urn:nbn:de:0030-drops-226272},
doi = {10.4230/TGDK.2.3},
annote = {Keywords: TGDK, Volume 2, Issue 3, 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 3, pp. 0:i-0:viii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{TGDK.2.3.0,
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 = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.3.0},
URN = {urn:nbn:de:0030-drops-226254},
doi = {10.4230/TGDK.2.3.0},
annote = {Keywords: Front Matter, Table of Contents, List of Authors}
}
Document
Unified Multimedia Segmentation - A Comprehensive Model for URI-based Media Segment Representation
Abstract
In multimedia annotation, referencing specific segments of a document is often desired due to its richness and multimodality, but no universal representation for such references exists. This significantly hampers the usage of multimedia content in knowledge graphs, as it is modeled as one large atomic information container. Unstructured data - such as text, audio, images, and video - can commonly be decomposed into its constituent parts, as such documents rarely contain only one semantic concept. Hence, it is reasonable to assume that these advances will make it possible to decompose these previous atomic components into logical segments. To be processable by the knowledge graph stack, however, one needs to break the atomic nature of multimedia content, providing a mechanism to address media segments.
This paper proposes a Unified Segmentation Model capable of depicting arbitrary segmentations on any media document type. The work begins with a formal analysis of multimedia and segmentation, exploring segmentation operations and how to describe them. Building on this analysis, it then develops a practical scheme for expressing segmentation in Uniform Resource Identifiers (URIs). Given that this approach makes segments of multimedia content referencable, it breaks their atomic nature and makes them first-class citizens within knowledge graphs. The proposed model is implemented as a proof of concept in the MediaGraph Store, a multimedia knowledge graph storage and querying engine.
Cite as
Jan Willi, Abraham Bernstein, and Luca Rossetto. Unified Multimedia Segmentation - A Comprehensive Model for URI-based Media Segment Representation. In Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 3, pp. 1:1-1:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{willi_et_al:TGDK.2.3.1,
author = {Willi, Jan and Bernstein, Abraham and Rossetto, Luca},
title = {{Unified Multimedia Segmentation - A Comprehensive Model for URI-based Media Segment Representation}},
journal = {Transactions on Graph Data and Knowledge},
pages = {1:1--1:34},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.3.1},
URN = {urn:nbn:de:0030-drops-225953},
doi = {10.4230/TGDK.2.3.1},
annote = {Keywords: Multimodal Knowledge Graphs, Multimedia Segmentation, Multimedia Representation}
}
Document
Strong Faithfulness for ELH Ontology Embeddings
Abstract
Ontology embedding methods are powerful approaches to represent and reason over structured knowledge in various domains. One advantage of ontology embeddings over knowledge graph embeddings is their ability to capture and impose an underlying schema to which the model must conform. Despite advances, most current approaches do not guarantee that the resulting embedding respects the axioms the ontology entails. In this work, we formally prove that normalized ELH has the strong faithfulness property on convex geometric models, which means that there is an embedding that precisely captures the original ontology. We present a region-based geometric model for embedding normalized ELH ontologies into a continuous vector space. To prove strong faithfulness, our construction takes advantage of the fact that normalized ELH has a finite canonical model. We first prove the statement assuming (possibly) non-convex regions, allowing us to keep the required dimensions low. Then, we impose convexity on the regions and show the property still holds. Finally, we consider reasoning tasks on geometric models and analyze the complexity in the class of convex geometric models used for proving strong faithfulness.
Cite as
Victor Lacerda, Ana Ozaki, and Ricardo Guimarães. Strong Faithfulness for ELH Ontology Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 3, pp. 2:1-2:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{lacerda_et_al:TGDK.2.3.2,
author = {Lacerda, Victor and Ozaki, Ana and Guimar\~{a}es, Ricardo},
title = {{Strong Faithfulness for ELH Ontology Embeddings}},
journal = {Transactions on Graph Data and Knowledge},
pages = {2:1--2:29},
ISSN = {2942-7517},
year = {2024},
volume = {2},
number = {3},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.3.2},
URN = {urn:nbn:de:0030-drops-225965},
doi = {10.4230/TGDK.2.3.2},
annote = {Keywords: Knowledge Graph Embeddings, Ontologies, Description Logic}
}