An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection

• URL: http://arxiv.org/abs/2203.05147v1
• Date: Thu, 10 Mar 2022 04:08:53 GMT
• Title: An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
• Authors: Shengxuan Luo, Sheng Yu
• Abstract summary: We propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED) We construct a medical cross-lingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks. In the EA task, UED achieves EA results comparable to those of state-of-the-art supervised EA baselines and outperforms the current state-of-the-art EA methods by combining supervised EA data.
• Score: 0.3965019866400874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is labor-consuming. In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED. The UED mines the literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA and then utilizes the EA results to assist the DED. We construct a medical cross-lingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks. Extensive experiments demonstrate that in the EA task, UED achieves EA results comparable to those of state-of-the-art supervised EA baselines and outperforms the current state-of-the-art EA methods by combining supervised EA data. For the DED task, UED obtains high-quality results without supervision.

Related papers

• DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs [3.500936203815729]
  We propose a dense entity retrieval framework for Entity Alignment (EA) We leverage language models to uniformly encode various features of entities and facilitate nearest entity search across Knowledge Graphs (KGs) Our approach achieves state-of-the-art performance compared to existing EA methods.
  arXiv  Detail & Related papers  (2024-08-02T10:12:42Z)
• Entity Disambiguation via Fusion Entity Decoding [68.77265315142296]
  We propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. We observe +1.5% improvements in end-to-end entity linking in the GERBIL benchmark compared with EntQA.
  arXiv  Detail & Related papers  (2024-04-02T04:27:54Z)
• Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases [49.86384156476041]
  We propose the framework textitLambda for dangling detection and entity alignment. Lambda features a GNN-based encoder called KEESA with spectral contrastive learning for EA and a positive-unlabeled learning algorithm for dangling detection called iPULE.
  arXiv  Detail & Related papers  (2024-03-16T17:21:58Z)
• 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)
• A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories [122.11358440078581]
  offline imitation is useful in real-world scenarios where arbitrary interactions are costly and expert actions are unavailable. We propose Trajectory-Aware Learning from Observations (TAILO) to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available.
  arXiv  Detail & Related papers  (2023-11-02T15:41:09Z)
• Unsupervised Entity Alignment for Temporal Knowledge Graphs [24.830107011195302]
  We present DualMatch, which fuses temporal and relational information for entity alignment. It is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Experiments on three real-world TKG datasets offer the insight that DualMatch outperforms the state-of-the-art methods in terms of H@1 by 2.4% - 10.7%.
  arXiv  Detail & Related papers  (2023-02-01T23:03:22Z)
• Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning [64.78972193105443]
  This paper presents a novel AE detection framework, named trustworthy for predictions. performs the detection by distinguishing the AE's abnormal relation with its augmented versions. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the label.
  arXiv  Detail & Related papers  (2022-08-31T08:18:44Z)
• High-quality Task Division for Large-scale Entity Alignment [28.001266850114643]
  DivEA achieves higher EA performance than alternative state-of-the-art solutions. We devise a counterpart discovery method that exploits the locality principle of the EA task and the power of trained EA models.
  arXiv  Detail & Related papers  (2022-08-22T14:46:38Z)
• ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment [27.449414854756913]
  Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision. We propose an interactive contrastive learning model for self-supervised EA.
  arXiv  Detail & Related papers  (2022-01-17T06:04:00Z)
• 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)
• Visual Pivoting for (Unsupervised) Entity Alignment [93.82387952905756]
  This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs) We show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment.
  arXiv  Detail & Related papers  (2020-09-28T20:09:40Z)

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.