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Learn Logic Rules for Reasoning on Knowledge Graphs.

RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs

ICLR, (2021)

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摘要

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 to other tasks, and hence are critical to learn. Existing methods either suffer from the problem of searching in a large search space (e.g., neural logic p...更多

代码

https://github.com/DeepGraphLearning/RNNLogic, Offical codes

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简介
  • Knowledge graphs are collections of real-world facts, which are useful in various applications.
  • This paper studies learning logic rules for reasoning on knowledge graphs.
  • The rule can be applied to infer new hobbies of people.
  • Such logic rules are able to improve interpretability and precision of reasoning (Qu & Tang, 2019; Zhang et al, 2020).
  • Due to the large search space, inferring high-quality logic rules for reasoning on knowledge graphs is a challenging task
重点内容
  • Knowledge graphs are collections of real-world facts, which are useful in various applications
  • Given the ranks from all queries, we report the Mean Rank (MR), Mean Reciprocal Rank (MRR) and Hit@k (H@k) under the filtered setting (Bordes et al, 2013), which is used by most existing studies
  • The reason is that RNNLogic is optimized with an EM-based framework, in which the reasoning predictor provides more useful feedback to the rule generator, and addresses the challenge of sparse reward
  • This paper studies learning logic rules for knowledge graph reasoning, and an approach called RNNLogic is proposed
  • RNNLogic treats a set of logic rules as a latent variable, and a rule generator as well as a reasoning predictor with logic rules are jointly learned
  • We see that RNNLogic significantly outperforms RotatE at every embedding dimension
  • Extensive expemriments prove the effectiveness of RNNLogic
方法
  • Experimental Setup of RNNLogic

    For each training triplet (h, r, t), the authors add an inverse triplet (t, r−1, h) into the training set, yielding an augmented set of training triplets T.
  • To build a training instance from pdata, the authors first randomly sample a triplet (h, r, t) from T , and form an instance as (G = T \ {(h, r, t)}, q = (h, r, ?), a = t).
  • The authors use the sampled triplet (h, r, t) to construct the query and answer, and use the rest of triplets in T to form the background knowledge graph G.
  • The background knowledge graph G is formed with all the triplets in T.
结果
  • The authors first compare RNNLogic with rule learning methods.
  • RNNLogic achieves much better results than statistical relational learning methods (MLN, Boosted RDN, PathRank) and neural differentiable methods (NeuralLP, DRUM, NLIL, CTP).
  • This is because the rule generator and reasoning predictor of RNNLogic can collaborate with each other to reduce search space and learn better rules.
  • The reason is that RNNLogic is optimized with an EM-based framework, in which the reasoning predictor provides more useful feedback to the rule generator, and addresses the challenge of sparse reward
结论
  • This paper studies learning logic rules for knowledge graph reasoning, and an approach called RNNLogic is proposed.
  • RNNLogic treats a set of logic rules as a latent variable, and a rule generator as well as a reasoning predictor with logic rules are jointly learned.
  • The authors develop an EM-based algorithm for optimization.
  • Extensive expemriments prove the effectiveness of RNNLogic.
  • The authors plan to study generating more complicated logic rules rather than only compositional rules
表格
  • Table1: Results of reasoning on FB15k-237 and WN18RR. H@k is in %. [∗] means the numbers are taken from original papers. [†] means we rerun the methods with the same evaluation process
  • Table2: Results of reasoning on the Kinship and UMLS datasets. H@k is in %
  • Table3: Comparison between REINFORCE and EM
  • Table4: Case study of the rules generated by the rule generator
  • Table5: Statistics of datasets
  • Table6: Comparison with MultiHopKG
  • Table7: Logic rules learned by RNNLogic
Download tables as Excel
相关工作
  • Our work is related to existing efforts on learning logic rules for knowledge graph reasoning. Most traditional methods enumerate relational paths between query entities and answer entities as candidate logic rules, and further learn a scalar weight for each rule to assess the quality. Representative methods include Markov logic networks (Kok & Domingos, 2005; Richardson & Domingos, 2006; Khot et al, 2011), relational dependency networks (Neville & Jensen, 2007; Natarajan et al, 2010), rule mining algorithms (Galarraga et al, 2013; Meilicke et al, 2019), path ranking (Lao & Cohen, 2010; Lao et al, 2011) and probabilistic personalized page rank (ProPPR) algorithms (Wang et al, 2013; 2014a;b). Some recent methods extend the idea by simultaneously learning logic rules and the weights in a differentiable way, and most of them are based on neural logic programming (Rocktaschel & Riedel, 2017; Yang et al, 2017; Cohen et al, 2018; Sadeghian et al, 2019; Yang & Song, 2020) or neural theorem provers (Rocktaschel & Riedel, 2017; Minervini et al, 2020). These methods and our approach are similar in spirit, as they are all able to learn the weights of logic rules efficiently. However, these existing methods try to simultaneously learn logic rules and their weights, which is nontrivial in terms of optimization. The main innovation of our approach is to separate rule generation and rule weight learning by introducing a rule generator and a reasoning predictor respectively, which can mutually enhance each other. The rule generator generates a few high-quality logic rules, and the reasoning predictor only focuses on learning the weights of such high-quality rules, which significantly reduces the search space and leads to better reasoning results. Meanwhile, the reasoning predictor can in turn help identify some useful logic rules to improve the rule generator.
基金
  • We see that RNNLogic significantly outperforms RotatE at every embedding dimension
研究对象与分析
datasets: 4
Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step. Experiments on four datasets prove the effectiveness of RNNLogic. Experimental Setup of RNNLogic

For each training triplet (h, r, t), we add an inverse triplet (t, r−1, h) into the training set, yielding an augmented set of training triplets T

datasets: 4
Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step. Experiments on four datasets prove the effectiveness of RNNLogic. Knowledge graphs are collections of real-world facts, which are useful in various applications

datasets: 4
Datasets. We choose four datasets for evaluation, including FB15k-237 (Toutanova & Chen, 2015), WN18RR (Dettmers et al, 2018), Kinship and UMLS (Kok & Domingos, 2007). For Kinship and UMLS, there are no standard data splits, so we randomly sample 30% of all the triplets for training, 20% for validation, and the rest 50% for testing

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  • This paper focuses on compositional rules, which have the abbreviation form r ← r1 ∧ · · · ∧ rl and thus could be viewed a sequence of relations [r, r1, r2 · · · rl, rEND], where r is the query relation or the head of the rule, {ri}li=1 are the body of the rule, and rEND is a special relation indicating the end of the relation sequence. We introduce a rule generator RNNθ parameterized with an LSTM (Hochreiter & Schmidhuber, 1997) to model such sequences. Given the current relation sequence [r, r1, r2 · · · ri], RNNθ aims to generate the next relation ri+1 and meanwhile output the probability of ri+1. The detailed computational process towards the goal is summarized as follows:
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作者
Junkun Chen
Junkun Chen
Louis-Pascal Xhonneux
Louis-Pascal Xhonneux
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