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Found 10 papers, 3 papers with code
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information.
Refining neural network predictions using background knowledge
Using a new algorithm called Iterative Local Refinement (ILR), we combine refinement functions to find refined predictions for logical formulas of any complexity.
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems.
Analyzing Differentiable Fuzzy Implications
In this paper, we investigate how implications from the fuzzy logic literature behave in a differentiable setting.
Analyzing Differentiable Fuzzy Logic Operators
Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice.
Semi-Supervised Learning using Differentiable Reasoning
We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data.
Reinforcement Learning for Personalized Dialogue Management
Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e. g. take the personal context of users into account.
The sameAs Problem: A Survey on Identity Management in the Web of Data
In a decentralised knowledge representation system such as the Web of Data, it is common and indeed desirable for different knowledge graphs to overlap.
Observing LOD using Equivalent Set Graphs: it is mostly flat and sparsely linked
This paper presents an empirical study aiming at understanding the modeling style and the overall semantic structure of Linked Open Data.
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation.
