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Found 8 papers, 1 papers with code
Equivariant Quantum Graph Circuits
We investigate quantum circuits for graph representation learning, and propose equivariant quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong relational inductive bias for learning over graph-structured data.
Temporal Knowledge Graph Completion using Box Embeddings
Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Node Classification Meets Link Prediction on Knowledge Graphs
Node classification and link prediction are widely studied in graph representation learning.
The Surprising Power of Graph Neural Networks with Random Node Initialization
In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties.
BoxE: A Box Embedding Model for Knowledge Base Completion
Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB).
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On the Approximability of Weighted Model Integration on DNF Structures
Weighted model counting (WMC) consists of computing the weighted sum of all satisfying assignments of a propositional formula.
Dynamic Bayesian Ontology Languages
Many formalisms combining ontology languages with uncertainty, usually in the form of probabilities, have been studied over the years.
Adding Context to Knowledge and Action Bases
Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution.
