Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs

• URL: http://arxiv.org/abs/2012.05750v1
• Date: Thu, 10 Dec 2020 15:36:47 GMT
• Title: Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
• Authors: Simon Ott, Laura Graf, Asan Agibetov, Christian Meilicke, Matthias Samwald
• Abstract summary: We introduce the SAFRAN rule application framework which aggregates rules through a scalable clustering algorithm. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237. It exceeds the results of multiple established embedding-based algorithms on FB15K-237 and narrows the gap between rule-based and embedding-based algorithms on OpenBioLink.
• Score: 4.502717871564512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, its applicability to large-scale prediction tasks on complex biomedical knowledge bases is limited by long inference times and difficulties with aggregating predictions made by multiple rules. We improve upon AnyBURL by introducing the SAFRAN rule application framework which aggregates rules through a scalable clustering algorithm. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark OpenBioLink. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and narrows the gap between rule-based and embedding-based algorithms on OpenBioLink. We also show that SAFRAN increases inference speeds by up to two orders of magnitude.

Related papers

• Conformalized Link Prediction on Graph Neural Networks [8.807684750444626]
  Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. We introduce a distribution-free and model-agnostic uncertainty quantification approach to construct a predictive interval with a statistical guarantee for GNN-based link prediction.
  arXiv  Detail & Related papers  (2024-06-26T21:17:37Z)
• Improving rule mining via embedding-based link prediction [2.422410293747519]
  Rule mining on knowledge graphs allows for explainable link prediction. Several approaches combining the two families have been proposed in recent years. We propose a new way to combine the two families of approaches.
  arXiv  Detail & Related papers  (2024-06-14T15:53:30Z)
• Approximate inference of marginals using the IBIA framework [0.0]
  Exact inference of marginals in probabilistic graphical models (PGM) is known to be intractable. We propose a new algorithm for marginal inference that is based on the incremental build-infer-approximate (IBIA) paradigm. Our method gives either better or comparable accuracy than existing variational and sampling based methods, with smaller runtimes.
  arXiv  Detail & Related papers  (2023-06-01T04:24:21Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Robustification of Online Graph Exploration Methods [59.50307752165016]
  We study a learning-augmented variant of the classical, notoriously hard online graph exploration problem. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm.
  arXiv  Detail & Related papers  (2021-12-10T10:02:31Z)
• SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models [5.52834593453565]
  We introduce SAFRAN, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10. It exceeds the results of multiple established embedding-based algorithms on FB15K-237 and WN18RR and narrows the gap between rule-based and embedding-based algorithms on YAGO3-10.
  arXiv  Detail & Related papers  (2021-09-16T14:18:29Z)
• Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition [161.09660864941603]
  We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. We present a novel activation-based branching strategy and a BaB framework, named Branch and Dual Network Bound (BaDNB) BaDNB outperforms previous complete verification systems by a large margin, cutting average verification times by factors up to 50 on adversarial properties.
  arXiv  Detail & Related papers  (2021-04-14T09:22:42Z)
• Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction [11.927046591097623]
  Link prediction is a relatively under-studied graph learning task, with current state-of-the-art models based on one- or two-layers of shallow graph auto-encoder (GAE) architectures. In this paper, we focus on addressing a limitation of current methods for link prediction, which can only use shallow GAEs and variational GAEs. Our proposed methods innovatively incorporate standard auto-encoders (AEs) into the architectures of GAEs, where standard AEs are leveraged to learn essential, low-dimensional representations via seamlessly integrating the adjacency information and node features
  arXiv  Detail & Related papers  (2021-03-21T14:43:10Z)
• Learning, compression, and leakage: Minimising classification error via meta-universal compression principles [87.054014983402]
  A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding. Here we consider a NML-based decision strategy for supervised classification problems, and show that it attains PAC learning when applied to a wide variety of models. We show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.
  arXiv  Detail & Related papers  (2020-10-14T20:03:58Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.