What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2306.02622v1
• Date: Mon, 5 Jun 2023 06:50:09 GMT
• Title: What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
• Authors: Zequn Sun and Jiacheng Huang and Xiaozhou Xu and Qijin Chen and Weijun Ren and Wei Hu
• Abstract summary: We provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities.
• Score: 20.100378168629195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.

Related papers

• Simple Ingredients for Offline Reinforcement Learning [86.1988266277766]
  offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. We show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but different tasks is simply added to the offline buffer. We show that scale, more than algorithmic considerations, is the key factor influencing performance.
  arXiv  Detail & Related papers  (2024-03-19T18:57:53Z)
• Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation [32.91818862407071]
  We present a propagation perspective to analyze weakly supervised EA. We show that aggregation-based EA models seek propagation operators for pairwise entity similarities. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model.
  arXiv  Detail & Related papers  (2024-02-05T14:06:15Z)
• Generating Explanations to Understand and Repair Embedding-based Entity Alignment [15.608451451547067]
  We present the first framework that can generate explanations for understanding and repairing embedding-based EA results. Given an EA pair produced by an embedding model, we first compare its neighbor entities and relations to build a matching subgraph as a local explanation. We then construct an alignment dependency graph to understand the pair from an abstract perspective.
  arXiv  Detail & Related papers  (2023-12-08T07:27:26Z)
• Improving Knowledge Graph Entity Alignment with Graph Augmentation [11.1094009195297]
  Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. We propose graph augmentation to create two graph views for margin-based alignment learning and contrastive entity representation learning.
  arXiv  Detail & Related papers  (2023-04-28T01:22:47Z)
• Learnable Pillar-based Re-ranking for Image-Text Retrieval [119.9979224297237]
  Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. We propose a novel learnable pillar-based re-ranking paradigm for image-text retrieval.
  arXiv  Detail & Related papers  (2023-04-25T04:33:27Z)
• Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets [32.68422342604253]
  We study the performance of entity alignment (EA) methods in practical settings, specifically focusing on the alignment of highly heterogeneous KGs (HHKGs) Our findings reveal that, in aligning HHKGs, valuable structure information can hardly be exploited through message-passing and aggregation mechanisms. These findings shed light on the potential problems associated with the conventional application of GNN-based methods as a panacea for all EA datasets.
  arXiv  Detail & Related papers  (2023-04-07T04:10:26Z)
• Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment [100.19568734815732]
  Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. attribute triples can also provide crucial alignment signal but have not been well explored yet. We propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.
  arXiv  Detail & Related papers  (2020-10-07T08:03:58Z)
• HittER: Hierarchical Transformers for Knowledge Graph Embeddings [85.93509934018499]
  We propose Hitt to learn representations of entities and relations in a complex knowledge graph. Experimental results show that Hitt achieves new state-of-the-art results on multiple link prediction. We additionally propose a simple approach to integrate Hitt into BERT and demonstrate its effectiveness on two Freebase factoid answering datasets.
  arXiv  Detail & Related papers  (2020-08-28T18:58:15Z)
• Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
  We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning. We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
  arXiv  Detail & Related papers  (2020-07-23T14:00:34Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• Building and Interpreting Deep Similarity Models [0.0]
  We propose to make similarities interpretable by augmenting them with an explanation in terms of input features. We develop BiLRP, a scalable and theoretically founded method to systematically decompose similarity scores on pairs of input features.
  arXiv  Detail & Related papers  (2020-03-11T17:46:55Z)

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.