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Invited Paper

DOI: 10.4230/OASIcs.AIB.2022.5

Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)

Authors: Manfred Jaeger

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract

Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling tool for graph and network data. Though much of the work on GNNs has focused on graphs with a single edge relation, they have also been adapted to multi-relational graphs, including knowledge graphs. In such multi-relational domains, the objectives and possible applications of GNNs become quite similar to what for many years has been investigated and developed in the field of statistical relational learning (SRL). This article first gives a brief overview of the main features of GNN and SRL approaches to learning and reasoning with graph data. It analyzes then in more detail their commonalities and differences with respect to semantics, representation, parameterization, interpretability, and flexibility. A particular focus will be on relational Bayesian networks (RBNs) as the SRL framework that is most closely related to GNNs. We show how common GNN architectures can be directly encoded as RBNs, thus enabling the direct integration of "low level" neural model components with the "high level" symbolic representation and flexible inference capabilities of SRL.

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Manfred Jaeger. Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 5:1-5:42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{jaeger:OASIcs.AIB.2022.5,
  author =	{Jaeger, Manfred},
  title =	{{Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{5:1--5:42},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.5},
  URN =		{urn:nbn:de:0030-drops-160035},
  doi =		{10.4230/OASIcs.AIB.2022.5},
  annote =	{Keywords: Graph neural networks, Statistical relational learning}
}

Document

DOI: 10.4230/DagSemProc.05051.7

Importance Sampling on Relational Bayesian Networks

Authors: Manfred Jaeger

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract

We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Furthermore, we use in our experiments a model that illustrates a fully general way of translating the recent framework of Markov Logic Networks into Relational Bayesian Networks.

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Manfred Jaeger. Importance Sampling on Relational Bayesian Networks. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{jaeger:DagSemProc.05051.7,
  author =	{Jaeger, Manfred},
  title =	{{Importance Sampling on Relational Bayesian Networks}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.7},
  URN =		{urn:nbn:de:0030-drops-4116},
  doi =		{10.4230/DagSemProc.05051.7},
  annote =	{Keywords: Relational models, Importance Sampling}
}

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Documents authored by Jaeger, Manfred

Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)

Abstract

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Importance Sampling on Relational Bayesian Networks

Abstract

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