We propose a novel black-box approach for performing adversarial attacks against
knowledge graph embedding models. An adversarial attack is a small perturbation of the …

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To
augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) …

Knowledge graph embedding (KGE) models are often used to predict missing links for
knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally …

In this paper we present a novel method, Knowledge Persistence (), for faster evaluation of
Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is …

Abstract Knowledge Base Embedding (KBE) models have been widely used to encode
structured information from knowledge bases, including WordNet. However, the existing …

Explanations for AI are expected to help human users understand AI-driven predictions.
Evaluating plausibility, the helpfulness of the explanations, is therefore essential for …

The standard evaluation protocol for measuring the quality of Knowledge Graph Completion
methods-the task of inferring new links to be added to a graph-typically involves a step …

To increase the utility of semantic industrial information models we propose a methodology
to incorporate extracted operator knowledge, which we assume to be present in the form of …

Abstract Knowledge graph embeddings, or KGEs, are models that learn vector
representations of knowledge graphs. These representations have been used for tasks such …

Knowledge graph embeddings, or KGEs, are models that learn vector representations of
knowledge graphs. These representations have been used for tasks such as predicting …