4 Search Results for "Vaisman, Alejandro"

Document
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DOI: 10.4230/TGDK.4.1.2
Temporal Modelling in Cultural Heritage Knowledge Graphs: Use Cases, Requirements, Evaluation, and Decision Support

Authors: Oleksandra Bruns, Jörg Waitelonis, Jeff Z. Pan, and Harald Sack

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

Abstract
Our culture, history and world are in constant motion, continuously shaped by the flow of time, evolving narratives, and shifting relationships. Capturing this temporal complexity within cultural heritage (CH) knowledge graphs is essential for preserving the dynamic nature of human heritage. However, standard RDF predicates fail to effectively model the temporal aspects of cultural data, such as changing facts, evolving relationships, and temporal concepts. Over the past two decades, a variety of RDF-based approaches have been proposed to address this limitation, yet guidance is missing on which method best suits specific CH contexts. This paper presents a systematic evaluation of temporal RDF modelling approaches from a CH perspective. Based on an analysis of real-world CH use cases, core temporal requirements are identified that reflect both modelling expressivity and practical concerns. Six prominent approaches - RDF*, tRDF, Named Graphs, Singleton Property, N-ary Relations, and 4D Fluents - are assessed across these requirements. Our findings reveal that no single solution fits all scenarios, but suitable approaches can be selected based on project-specific priorities. To support practitioners, a decision-support tool is introduced to guide them in selecting the most suitable extension for their specific needs. This work provides practical guidance for CH modelling and contributes to the broader development of temporally aware Linked Data.
Cite as

Oleksandra Bruns, Jörg Waitelonis, Jeff Z. Pan, and Harald Sack. Temporal Modelling in Cultural Heritage Knowledge Graphs: Use Cases, Requirements, Evaluation, and Decision Support. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 2:1-2:46, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{bruns_et_al:TGDK.4.1.2,
  author =	{Bruns, Oleksandra and Waitelonis, J\"{o}rg and Pan, Jeff Z. and Sack, Harald},
  title =	{{Temporal Modelling in Cultural Heritage Knowledge Graphs: Use Cases, Requirements, Evaluation, and Decision Support}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:46},
  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.4.1.2},
  URN =		{urn:nbn:de:0030-drops-256871},
  doi =		{10.4230/TGDK.4.1.2},
  annote =	{Keywords: Temporal Data Representation, RDF Extensions, Cultural Heritage, Knowledge Graphs}
}
Document
DOI: 10.4230/LIPIcs.TIME.2025.9
Temporal GraphQL: A Tree Grammar Approach

Authors: Curtis E. Dyreson and Bishal Sarkar

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
This paper presents a novel system, called Temporal GraphQL, for supporting temporal data in web services. A temporal web service is a service that provides a temporal view of data, that is, a view of the current data as well as past or future states of the data. Capturing the history of the data is important in data forensics, data auditing, and subscriptions, where an application continuously reads data. GraphQL is a technology for improving the development and management of web services. Originally developed by Facebook and widely used in industry, GraphQL is a query language for web services. This paper introduces Temporal GraphQL. We show how to use tree grammars to model GraphQL schemas, data, and queries, and propose temporal tree grammars to model Temporal GraphQL. We extend GraphQL with temporal snapshot, slice, and delta operators. To the best of our knowledge, this is the first work on Temporal GraphQL and temporal tree grammars.
Cite as

Curtis E. Dyreson and Bishal Sarkar. Temporal GraphQL: A Tree Grammar Approach. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dyreson_et_al:LIPIcs.TIME.2025.9,
  author =	{Dyreson, Curtis E. and Sarkar, Bishal},
  title =	{{Temporal GraphQL: A Tree Grammar Approach}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.9},
  URN =		{urn:nbn:de:0030-drops-244556},
  doi =		{10.4230/LIPIcs.TIME.2025.9},
  annote =	{Keywords: Temporal databases, temporal queries, GraphQL, web services}
}
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/DagSemProc.08471.4
Temporal Support of Regular Expressions in Sequential Pattern Mining

Authors: Alejandro Vaisman, Leticia I. Gómez, and Bart Kuijpers

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Abstract
Classic algorithms for sequential pattern discovery,return all frequent sequences present in a database. Since, in general, only a few ones are interesting from a user's point of view, languages based on regular expressions (RE) have been proposed to restrict frequent sequences to the ones that satisfy user-specified constraints. Although the support of a sequence is computed as the number of data-sequences satisfying a pattern with respect to the total number of data-sequences in the database, once regular expressions come into play, new approaches to the concept of support are needed. For example, users may be interested in computing the support of the RE as a whole, in addition to the one of a particular pattern. As a simple example, the expression $(A|B).C$ is satisfied by sequences like A.C or B.C. Even though the semantics of this RE suggests that both of them are equally interesting to the user, if neither of them verifies a minimum support although together they do), they would not be retrieved. Also, when the items are frequently updated, the traditional way of counting support in sequential pattern mining may lead to incorrect (or, at least incomplete), conclusions. For example, if we are looking for the support of the sequence A.B, where A and B are two items such that A was created after B, all sequences in the database that were completed before A was created, can never produce a match. Therefore, accounting for them would underestimate the support of the sequence A.B. The problem gets more involved if we are interested in categorical sequential patterns. In light of the above, in this paper we propose to revise the classic notion of support in sequential pattern mining, introducing the concept of temporal support of regular expressions, intuitively defined as the number of sequences satisfying a target pattern, out of the total number of sequences that could have possibly matched such pattern, where the pattern is defined as a RE over complex items (i.e., not only item identifiers, but also attributes and functions). We present and discuss a theoretical framework for these novel notion of support.
Cite as

Alejandro Vaisman, Leticia I. Gómez, and Bart Kuijpers. Temporal Support of Regular Expressions in Sequential Pattern Mining. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{vaisman_et_al:DagSemProc.08471.4,
  author =	{Vaisman, Alejandro and G\'{o}mez, Leticia I. and Kuijpers, Bart},
  title =	{{Temporal Support of Regular Expressions in Sequential Pattern Mining}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.4},
  URN =		{urn:nbn:de:0030-drops-20087},
  doi =		{10.4230/DagSemProc.08471.4},
  annote =	{Keywords: Temporal support, sequential pattern mining}
}
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