Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916326353666048 |
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| author | Zhu, Yuqicheng Potyka, Nico Xiong, Bo Tran, Trung-Kien Nayyeri, Mojtaba Kharlamov, Evgeny Staab, Steffen |
| author_facet | Zhu, Yuqicheng Potyka, Nico Xiong, Bo Tran, Trung-Kien Nayyeri, Mojtaba Kharlamov, Evgeny Staab, Steffen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings Zhu, Yuqicheng Potyka, Nico Xiong, Bo Tran, Trung-Kien Nayyeri, Mojtaba Kharlamov, Evgeny Staab, Steffen Artificial Intelligence 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. |
| title | Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.11821 |