COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing

• URL: http://arxiv.org/abs/2405.13602v1
• Date: Wed, 22 May 2024 12:53:12 GMT
• Title: COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing
• Authors: Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan,
• Abstract summary: Knowledge graph entity typing aims to infer missing entity type instances in knowledge graphs. Previous research has predominantly centered around leveraging contextual information associated with entities. This paper introduces Cross-view Optimal Transport for knowledge graph Entity Typing.
• Score: 27.28214706269035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowledge graph entity typing (KGET) aims to infer missing entity type instances in knowledge graphs. Previous research has predominantly centered around leveraging contextual information associated with entities, which provides valuable clues for inference. However, they have long ignored the dual nature of information inherent in entities, encompassing both high-level coarse-grained cluster knowledge and fine-grained type knowledge. This paper introduces Cross-view Optimal Transport for knowledge graph Entity Typing (COTET), a method that effectively incorporates the information on how types are clustered into the representation of entities and types. COTET comprises three modules: i) Multi-view Generation and Encoder, which captures structured knowledge at different levels of granularity through entity-type, entity-cluster, and type-cluster-type perspectives; ii) Cross-view Optimal Transport, transporting view-specific embeddings to a unified space by minimizing the Wasserstein distance from a distributional alignment perspective; iii) Pooling-based Entity Typing Prediction, employing a mixture pooling mechanism to aggregate prediction scores from diverse neighbors of an entity. Additionally, we introduce a distribution-based loss function to mitigate the occurrence of false negatives during training. Extensive experiments demonstrate the effectiveness of COTET when compared to existing baselines.

Related papers

• SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment [13.487673375206276]
  This paper presents a soft label propagation framework that integrates multi-source data and iterative seed enhancement. A bidirectional weighted joint loss function is implemented, which reduces the distance between positive samples and differentially processes negative samples. Our method outperforms existing semi-supervised approaches, as evidenced by superior results on multiple datasets.
  arXiv  Detail & Related papers  (2024-10-28T04:50:46Z)
• Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs [25.399684403558553]
  We propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing (MCLET) MCLET effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings.
  arXiv  Detail & Related papers  (2023-10-18T14:41:09Z)
• Ontology Enrichment for Effective Fine-grained Entity Typing [45.356694904518626]
  Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly. We develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples.
  arXiv  Detail & Related papers  (2023-10-11T18:30:37Z)
• Investigating Graph Structure Information for Entity Alignment with Dangling Cases [31.779386064600956]
  Entity alignment aims to discover the equivalent entities in different knowledge graphs (KGs) We propose a novel entity alignment framework called Weakly-optimal Graph Contrastive Learning (WOGCL) We show that WOGCL outperforms the current state-of-the-art methods with pure structural information in both traditional (relaxed) and dangling settings.
  arXiv  Detail & Related papers  (2023-04-10T17:24:43Z)
• Prototype-based Embedding Network for Scene Graph Generation [105.97836135784794]
  Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. Due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category. Prototype-based Embedding Network (PE-Net) models entities/predicates with prototype-aligned compact and distinctive representations. PL is introduced to help PE-Net efficiently learn such entitypredicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching.
  arXiv  Detail & Related papers  (2023-03-13T13:30:59Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• EchoEA: Echo Information between Entities and Relations for Entity Alignment [1.1470070927586016]
  We propose a novel framework, Echo Entity Alignment (EchoEA), which leverages self-attention mechanism to spread entity information to relations and echo back to entities. The experimental results on three real-world cross-lingual datasets are stable at around 96% at hits@1 on average.
  arXiv  Detail & Related papers  (2021-07-07T07:34:21Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• Interpretable Entity Representations through Large-Scale Typing [61.4277527871572]
  We present an approach to creating entity representations that are human readable and achieve high performance out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types. We show that it is possible to reduce the size of our type set in a learning-based way for particular domains.
  arXiv  Detail & Related papers  (2020-04-30T23:58:03Z)

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.