24 citations · 40 across the 21 of their papers we have counts for
8 papers · 1 filter
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning
Soyoung Oh, Nursulu Sagimbayeva, Nicholas Jennings +3
In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting or judging the agent's subseque…
Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs
Johannes Mäkelburg, Yiwen Peng, Mehwish Alam +2
Despite the vast amount of information encoded in Knowledge Graphs (KGs), information about the class affiliation of entities remains often incomplete. Graph Convolutional Networks…
Neurosymbolic Methods for Dynamic Knowledge Graphs
Mehwish Alam, Genet Asefa Gesese, Pierre-Henri Paris
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the add…
Refining Wikidata Taxonomy using Large Language Models
Yiwen Peng, Thomas Bonald, Mehwish Alam
Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxono…
Neurosymbolic Methods for Rule Mining
Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam +3
In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining met…
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Mehwish Alam, Frank van Harmelen, Maribel Acosta
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph…
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs
Genet Asefa Gesese, Harald Sack, Mehwish Alam
Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these…
A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
Cristian Santini, Genet Asefa Gesese, Silvio Peroni +3
Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to…