RNNLogic: Learning Logic Rules for Reasoning on. . RNNLogic treats logic rules as a latent variable, and simultaneously trains a rule generator as well as a reasoning predictor with logic rules. We develop an EM-based.
RNNLogic: Learning Logic Rules for Reasoning on. from images.deepai.org
To address these limitations, this paper proposes a probabilistic model called RNNLogic. RNNLogic treats logic rules as a latent variable, and simultaneously trains a rule generator.
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graphs is to predict missing facts by reasoning with existing ones, a.k.a. knowledge graph reasoning. This paper studies learning logic rules for reasoning on knowledge graphs..
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2017. TLDR. A novel reinforcement learning framework for learning multi-hop relational paths is described, which uses a policy-based agent with continuous states based on knowledge.
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Experiments on four datasets prove the effectiveness of RNNLogic, a probabilistic model that treats logic rules as a latent variable, and simultaneously trains a rule generator as well as a.
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This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize.
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Figure 2: Performance w.r.t. # logic rules. RNNLogic achieves competitive results even with 10 rules per query relation. 100 200 500 1000 2000 # Embedding Dimension "RNNLogic:.
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An EM-based algorithm for optimization of a probabilistic model called RNNLogic, which treats logic rules as a latent variable, and simultaneously trains a rule generator as well.
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RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. Click To Get Model/Code. This paper studies learning logic rules for reasoning on knowledge graphs..
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Logic Rule. We perform knowledge graph reasoning by learning logic rules, where logic rules in this paper have the conjunctive form 8fX igl i=0 r(X 0;X l) r 1(X 0;X 1)^^ r l(X l 1;X l) with.
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RNNLogic treats logic rules as a latent variable, and simultaneously trains a rule generator as well as a reasoning predictor with logic rules. We develop an EM-based algorithm for.
Source: img-blog.csdnimg.cn
This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize.
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Table 2: Results of knowledge graph reasoning on the FB15k-237 and WN18RR datasets with only (h,r, ?)-queries. H@k is in %. [∗] means that the numbers are taken from the original.
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Rnnlogic: Learning logic rules for reasoning on knowledge graphs. arXiv preprint arXiv:2010.04029 (2020). Google Scholar; Meng Qu and Jian Tang. 2019. Probabilistic logic.
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Logic Rule. We perform knowledge graph reasoning by learning logic rules, where logic rules in this paper have the conjunctive form 8fX igl i=0 r(X 0;X l) r 1(X 0;X 1) ^ ^ r l(X l 1;X l).
