Asymptotic elimination of partially continuous aggregation functions in directed graphical models

Koponen, Vera; Weitkämper, Felix

doi:10.1016/j.ic.2023.105061

Cited by 1 publication

(1 citation statement)

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“…Automated theorem provers provide algorithmic solutions for the reasoning tasks described in Example 5.3. For the analysis of limit probabilities as in Example 5.5 there exist a few theoretical results that puts them into the reach of general automated inference methods (Grandjean, 1983 ; Jaeger, 1998 ; Cozman and Mauá, 2019 ; Koponen and Weitkämper, 2023 ). However, these results have not yet been carried over to operational implementations.…”

Section: Reasoning: Tasks and Techniquesmentioning

confidence: 99%

Learning and reasoning with graph data

Jaeger

2023

Front. Artif. Intell.

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Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields ranging from logic-oriented knowledge representation and reasoning to graph kernels and statistical relational learning. In this review we outline a broad map and inventory of the field of learning and reasoning with graphs that spans the spectrum from reasoning in the form of logical deduction to learning node embeddings. To obtain a unified perspective on such a diverse landscape we introduce a simple and general semantic concept of a model that covers logic knowledge bases, graph neural networks, kernel support vector machines, and many other types of frameworks. Still at a high semantic level, we survey common strategies for model specification using probabilistic factorization and standard feature construction techniques. Based on this semantic foundation we introduce a taxonomy of reasoning tasks that casts problems ranging from transductive link prediction to asymptotic analysis of random graph models as queries of different complexities for a given model. Similarly, we express learning in different frameworks and settings in terms of a common statistical maximum likelihood principle. Overall, this review aims to provide a coherent conceptual framework that provides a basis for further theoretical analyses of respective strengths and limitations of different approaches to handling graph data, and that facilitates combination and integration of different modeling paradigms.

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Section: Reasoning: Tasks and Techniquesmentioning

confidence: 99%

Learning and reasoning with graph data

Jaeger

2023

Front. Artif. Intell.

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Asymptotic elimination of partially continuous aggregation functions in directed graphical models

Cited by 1 publication

References 20 publications

Learning and reasoning with graph data

Learning and reasoning with graph data

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