4 Search Results for "Tucker, Catherine"

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
Vision
DOI: 10.4230/TGDK.1.1.6
Towards Ordinal Data Science

Authors: Gerd Stumme, Dominik Dürrschnabel, and Tom Hanika

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
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)

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@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
DOI: 10.4230/DagRep.12.9.150
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)

Authors: Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today’s scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena - and leveraging scientific advances to deliver innovative solutions to improve society’s health, wealth, and well-being - requires new ways of analysing complex systems. The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools. This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.
Cite as

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery. Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). In Dagstuhl Reports, Volume 12, Issue 9, pp. 150-199, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{berens_et_al:DagRep.12.9.150,
  author =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  title =	{{Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)}},
  pages =	{150--199},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.150},
  URN =		{urn:nbn:de:0030-drops-178125},
  doi =		{10.4230/DagRep.12.9.150},
  annote =	{Keywords: machine learning, artificial intelligence, life sciences, physical sciences, environmental sciences, simulation, causality, modelling}
}
Document
Invited Talk
DOI: 10.4230/OASIcs.Tokenomics.2021.12
Blockchain and Privacy (Invited Talk)

Authors: Catherine Tucker

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

Abstract
The unique value proposition of ’smart contracts' based on blockchain technology is the creation of a permanent public record of agreed-upon transactions that cannot be changed retroactively. Though this is attractive in terms of reducing the potential for fraud, a person entering into a smart contract pre-commits both their current self and their future selves, no matter what changes occur to them or to their circumstances. The advantages of such pre-commitments can be substantial, but even in an age of increasing adoption of distributed ledger technologies, self-reinvention remains important. From a surveillance perspective, it is important to prevent governments from reliably associating a particular cryptoasset transaction with a particular person. For individuals, it is important to preserve the ability to assume new identities both formally and informally. This presentation will present an expanded and refined understanding of what it means for a blockchain use case to "protect privacy," and in particular, how such use cases can encourage a notion of personal identity that is inflexible and matches poorly with individuals' notions regarding their identities. In addition I discuss how privacy regulation may itself shape the development of blockchain.
Cite as

Catherine Tucker. Blockchain and Privacy (Invited Talk). In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, p. 12:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{tucker:OASIcs.Tokenomics.2021.12,
  author =	{Tucker, Catherine},
  title =	{{Blockchain and Privacy}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{12:1--12:1},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.12},
  URN =		{urn:nbn:de:0030-drops-159093},
  doi =		{10.4230/OASIcs.Tokenomics.2021.12},
  annote =	{Keywords: Blockchain, Privacy}
}
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