RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding

• URL: http://arxiv.org/abs/2101.10070v1
• Date: Mon, 25 Jan 2021 13:31:29 GMT
• Title: RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
• Authors: Danushka Bollegala, Huda Hakami, Yuichi Yoshida and Ken-ichi Kawarabayashi
• Abstract summary: This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) We derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail) We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.
• Score: 50.010601631982425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses have been achieved, existing methods are heuristically motivated and theoretical understanding of such embeddings is comparatively underdeveloped. This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) to derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail). Moreover, we show that marginal loss minimisation, a popular objective used in much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. Using the derived objective, accurate KGEs are learnt from FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.

Related papers

• Path-based Explanation for Knowledge Graph Completion [17.541247786437484]
  Proper explanations for the results of GNN-based Knowledge Graph Completion models increase model transparency. Existing practices for explaining KGC tasks rely on instance/subgraph-based approaches. We propose Power-Link, the first path-based KGC explainer that explores GNN-based models.
  arXiv  Detail & Related papers  (2024-01-04T14:19:37Z)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning [49.09125100268454]
  Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Completion Graph (KGC) task. Relational patterns are an important factor in the performance of KGE models. We introduce a training-free method to enhance KGE models' performance over various relational patterns.
  arXiv  Detail & Related papers  (2023-08-15T17:30:57Z)
• CausE: Towards Causal Knowledge Graph Embedding [13.016173217017597]
  Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces. We build the new paradigm of KGE in the context of causality and embedding disentanglement. We propose a Causality-enhanced knowledge graph Embedding (CausE) framework.
  arXiv  Detail & Related papers  (2023-07-21T14:25:39Z)
• Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion [69.55700751102376]
  Few-shot knowledge graph completion (FKGC) aims to predict missing facts for unseen relations with few-shot associated facts. Existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC)
  arXiv  Detail & Related papers  (2023-04-17T11:42:28Z)
• Analogical Inference Enhanced Knowledge Graph Embedding [5.3821360049964815]
  We propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. An analogical object retriever retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. AnKGE achieves competitive results on link prediction task and well performs analogical inference.
  arXiv  Detail & Related papers  (2023-01-03T07:24:05Z)
• Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition [4.041834517339835]
  We propose a novel knowledge graph that incorporates tensor decomposition within the aggregation function of Graph Conalvolution Network (R-GCN) Our model enhances the representation of neighboring entities by employing projection matrices of a low-rank tensor defined by relation types. We adopt a training strategy inspired by contrastive learning to relieve the training limitation of the 1-k-k encoder method inherent in handling vast graphs.
  arXiv  Detail & Related papers  (2022-12-11T19:07:34Z)
• KGE-CL: Contrastive Learning of Knowledge Graph Embeddings [64.67579344758214]
  We propose a simple yet efficient contrastive learning framework for knowledge graph embeddings. It can shorten the semantic distance of the related entities and entity-relation couples in different triples. It can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.
  arXiv  Detail & Related papers  (2021-12-09T12:45:33Z)
• Embedding Graph Auto-Encoder for Graph Clustering [90.8576971748142]
  Graph auto-encoder (GAE) models are based on semi-supervised graph convolution networks (GCN) We design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding Graph Auto-Encoder (EGAE) EGAE consists of one encoder and dual decoders.
  arXiv  Detail & Related papers  (2020-02-20T09:53:28Z)

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.