808 citations · 1.4k across the 52 of their papers we have counts for
16 papers · 1 filter
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
Bhushan Kotnis, Carolin Lawrence, Mathias Niepert
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of p…
MMKG: Multi-Modal Knowledge Graphs
Ye Liu, Hui Li, Alberto Garcia-Duran +3
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KG…
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entit…
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally…
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
Alberto Garcia-Duran, Mathias Niepert
We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel…