1.8k citations · 2.1k across the 44 of their papers we have counts for
47 papers
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…
Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
Taewoon Kim, Vincent François-Lavet, Michael Cochez
Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns which symbolic m…
InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah +1
Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential Fir…
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Taewoon Kim, Vincent François-Lavet, Michael Cochez
Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term tr…
Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation
Yannick Brunink, Daniel Daza, Yunjie He +1
Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit graph structure, allowing them to…
SemBench: A Benchmark for Semantic Query Processing Engines
Jiale Lao, Andreas Zimmerer, Olga Ovcharenko +12
We present a benchmark targeting a novel class of systems: semantic query processing engines. Those systems rely inherently on generative and reasoning capabilities of state-of-the…