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Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
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Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the example of Statistical EL (SEL), a statistical extension of the lightweight Description Logic EL. We provide proofs for runtime and soundness guarantees, and empirically evaluate the runtime and approximation quality of our approach.
Forward citations
Cited by 2 Pith papers
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ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
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SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs
SCAIR, a training-free schema-conditioned agentic KG-RAG method, substantially outperforms existing KG-RAG approaches on a new enterprise CMDB benchmark, but the evaluation has notable confounds.
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