2 Search Results for "Vogiatzis, Dimitrios"

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DOI: 10.4230/TGDK.2.1.3
Semantic Web: Past, Present, and Future

Authors: Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

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
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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
DOI: 10.4230/LIPIcs.TIME.2018.4
Predicting the Evolution of Communities with Online Inductive Logic Programming

Authors: George Athanasopoulos, George Paliouras, Dimitrios Vogiatzis, Grigorios Tzortzis, and Nikos Katzouris

Published in: LIPIcs, Volume 120, 25th International Symposium on Temporal Representation and Reasoning (TIME 2018)

Abstract
In the recent years research on dynamic social network has increased, which is also due to the availability of data sets from streaming media. Modeling a network's dynamic behaviour can be performed at the level of communities, which represent their mesoscale structure. Communities arise as a result of user to user interaction. In the current work we aim to predict the evolution of communities, i.e. to predict their future form. While this problem has been studied in the past as a supervised learning problem with a variety of classifiers, the problem is that the "knowledge" of a classifier is opaque and consequently incomprehensible to a human. Thus we have employed first order logic, and in particular the event calculus to represent the communities and their evolution. We addressed the problem of predicting the evolution as an online Inductive Logic Programming problem (ILP), where the issue is to learn first order logical clauses that associate evolutionary events, and particular Growth, Shrinkage, Continuation and Dissolution to lower level events. The lower level events are features that represent the structural and temporal characteristics of communities. Experiments have been performed on a real life data set form the Mathematics StackExchange forum, with the OLED framework for ILP. In doing so we have produced clauses that model both short term and long term correlations.
Cite as

George Athanasopoulos, George Paliouras, Dimitrios Vogiatzis, Grigorios Tzortzis, and Nikos Katzouris. Predicting the Evolution of Communities with Online Inductive Logic Programming. In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{athanasopoulos_et_al:LIPIcs.TIME.2018.4,
  author =	{Athanasopoulos, George and Paliouras, George and Vogiatzis, Dimitrios and Tzortzis, Grigorios and Katzouris, Nikos},
  title =	{{Predicting the Evolution of Communities with Online Inductive Logic Programming}},
  booktitle =	{25th International Symposium on Temporal Representation and Reasoning (TIME 2018)},
  pages =	{4:1--4:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-089-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{120},
  editor =	{Alechina, Natasha and N{\o}rv\r{a}g, Kjetil and Penczek, Wojciech},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2018.4},
  URN =		{urn:nbn:de:0030-drops-97691},
  doi =		{10.4230/LIPIcs.TIME.2018.4},
  annote =	{Keywords: Social Network Analysis, Community Evolution Prediction, Machine Learning, Inductive Logic Programming, Event Calculus, Online Learning}
}
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