Do Similar Entities have Similar Embeddings?

• URL: http://arxiv.org/abs/2312.10370v2
• Date: Thu, 28 Mar 2024 09:12:21 GMT
• Title: Do Similar Entities have Similar Embeddings?
• Authors: Nicolas Hubert, Heiko Paulheim, Armelle Brun, Davy Monticolo,
• Abstract summary: Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space. Yet, the relation of entity similarity and similarity in the embedding space has rarely been formally evaluated. This paper challenges the prevailing assumption that entity similarity in the graph is inherently mirrored in the embedding space.
• Score: 2.9498907601878974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i.e.}, position similar entities within the graph close to one another. This desirable property make KGEMs widely used in downstream tasks such as recommender systems or drug repurposing. Yet, the relation of entity similarity and similarity in the embedding space has rarely been formally evaluated. Typically, KGEMs are assessed based on their sole link prediction capabilities, using ranked-based metrics such as Hits@K or Mean Rank. This paper challenges the prevailing assumption that entity similarity in the graph is inherently mirrored in the embedding space. Therefore, we conduct extensive experiments to measure the capability of KGEMs to cluster similar entities together, and investigate the nature of the underlying factors. Moreover, we study if different KGEMs expose a different notion of similarity. Datasets, pre-trained embeddings and code are available at: https://github.com/nicolas-hbt/similar-embeddings/.

Related papers

• Cluster-Aware Similarity Diffusion for Instance Retrieval [64.40171728912702]
  Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. We propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval.
  arXiv  Detail & Related papers  (2024-06-04T14:19:50Z)
• Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE [3.174882428337821]
  Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. In this work, we design protographs -- small, modified versions of a KG that leverage RDF/S information. The learnt protograph-based embeddings are meant to encapsulate the semantics of a KG, and can be leveraged in learning KGEs that, in turn, also better capture semantics.
  arXiv  Detail & Related papers  (2023-06-06T13:22:54Z)
• Sem@$K$: Is my knowledge graph embedding model semantic-aware? [1.8024397171920883]
  We extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w.r.t. domain and range constraints. Our experiments show that Sem@K provides a new perspective on KGEM quality. Some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics.
  arXiv  Detail & Related papers  (2023-01-13T15:06:47Z)
• Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities [55.952077365016066]
  An empirical observation is that the head and tail entities connected by the same relation often share similar semantic attributes. We propose a novel approach, which introduces a set of virtual nodes called textittextbfrelational prototype entities. By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities.
  arXiv  Detail & Related papers  (2022-06-16T09:25:33Z)
• Attributable Visual Similarity Learning [90.69718495533144]
  This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph. Experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods.
  arXiv  Detail & Related papers  (2022-03-28T17:35:31Z)
• Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings [70.390286614242]
  We propose a novel regularizer -- namely, DUality-induced RegulArizer (DURA) -- which effectively encourages the entities with similar semantics to have similar embeddings. Experiments demonstrate that DURA consistently and significantly improves the performance of state-of-the-art semantic matching models.
  arXiv  Detail & Related papers  (2022-03-24T09:24:39Z)
• Towards Similarity-Aware Time-Series Classification [51.2400839966489]
  We study time-series classification (TSC), a fundamental task of time-series data mining. We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2022-01-05T02:14:57Z)
• Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding [22.123001954919893]
  We propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-12T11:27:46Z)
• RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding [50.010601631982425]
  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.
  arXiv  Detail & Related papers  (2021-01-25T13:31:29Z)

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.