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We propose a framework, Neural Logic Programming, that\ncombines the parameter and structure learning of first-order logical rules in\nan end-to-end differentiable model. This approach is inspired by a\nrecently-developed differentiable logic called TensorLog, where inference tasks\ncan be compiled into sequences of differentiable operations. We design a neural\ncontroller system that learns to compose these operations. Empirically, our\nmethod outperforms prior work on multiple knowledge base benchmark datasets,\nincluding Freebase and WikiMovies.","url_abs":"http://arxiv.org/abs/1702.08367v3","url_pdf":"http://arxiv.org/pdf/1702.08367v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"differentiable-learning-of-logical-rules-for","repo_url":"https://github.com/fanyangxyz/Neural-LP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"differentiable-learning-of-logical-rules-for","repo_url":"https://github.com/kexinyi71/neural-lp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.08367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.08367"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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