Did you mean: Knowledge graph completion via probabilistic reasoning.
Graph feature models mine rule-like patterns from a knowledge base and use them to predict missing edges.
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Graph feature models mine rule-like patterns from a knowledge base and use them to predict missing edges. These models take account of the graph structure ...
In this paper, we develop an efficient model which uses association rules to make inferences. First, we use a rule mining model to detect simple association ...
Abstract. A case-based reasoning (CBR) system solves a new problem by retrieving 'cases' that are sim- ilar to the given problem. If such a system can.
Mar 9, 2023 · Probabilistic knowledge graphs, as developed by Accenture Labs, offer exactly this kind of numerical reasoning directly from your knowledge graph.
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Aug 21, 2016 · We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (eg syllogism)
Oct 7, 2020 · Abstract:A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem.
In this paper we present our idea of performing KGC by learning liftable probabilistic logic programs via regularization, using LIFTCOVER+, with the aim of ...
[PDF] Unsupervised Knowledge Graph Alignment by Probabilistic ... - IJCAI
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In this study, we aim at combining the above two solutions and thus pro- pose an iterative framework named PRASE that is based on probabilistic reasoning and ...
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Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases.