Unsupervised Robust Cross-Lingual Entity Alignment via Joint Modeling of Entity and Relation Texts
- URL: http://arxiv.org/abs/2407.15588v1
- Date: Mon, 22 Jul 2024 12:25:48 GMT
- Title: Unsupervised Robust Cross-Lingual Entity Alignment via Joint Modeling of Entity and Relation Texts
- Authors: Soojin Yoon, Sungho Ko, Tongyoung Kim, SeongKu Kang, Jinyoung Yeo, Dongha Lee,
- Abstract summary: Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages.
Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs.
We propose ERAlign, an unsupervised and robust cross-lingual EA framework.
- Score: 17.477542644785483
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge.Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs. To address this, recent studies have shifted towards a self-supervised and unsupervised frameworks. Despite their effectiveness, these approaches have limitations: (1) they mainly focus on entity features, neglecting the semantic information of relations, (2) they assume isomorphism between source and target graphs, leading to noise and reduced alignment accuracy, and (3) they are susceptible to noise in the textual features, especially when encountering inconsistent translations or Out-Of-Vocabulary (OOV) problems. In this paper, we propose ERAlign, an unsupervised and robust cross-lingual EA framework that jointly performs Entity-level and Relation-level Alignment using semantic textual features of relations and entities. Its refinement process iteratively enhances results by fusing entity-level and relation-level alignments based on neighbor triple matching. The additional verification process examines the entities' neighbor triples as the linearized text. This \textit{Align-and-Verify} pipeline that rigorously assesses alignment results, achieving near-perfect alignment even in the presence of noisy textual features of entities. Our extensive experiments demonstrate that robustness and general applicability of \proposed improved the accuracy and effectiveness of EA tasks, contributing significantly to knowledge-oriented applications.
Related papers
- Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models [31.443478448031886]
RoSE (Relation-oriented Semantic Edge-decomposition) is a novel framework that decomposes the graph structure by analyzing raw text attributes.
Our framework significantly enhances node classification performance across various datasets, with improvements of up to 16% on the Wisconsin dataset.
arXiv Detail & Related papers (2024-05-28T20:54:47Z) - Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph.
We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
arXiv Detail & Related papers (2023-07-19T02:11:19Z) - From Alignment to Entailment: A Unified Textual Entailment Framework for
Entity Alignment [17.70562397382911]
Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings.
We transform both triples into unified textual sequences, and model the EA task as a bi-directional textual entailment task.
Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
arXiv Detail & Related papers (2023-05-19T08:06:50Z) - Informed Multi-context Entity Alignment [27.679124991733907]
We propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues.
In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts.
holistic reasoning is used to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality.
Results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.
arXiv Detail & Related papers (2022-01-02T06:29:30Z) - SAIS: Supervising and Augmenting Intermediate Steps for Document-Level
Relation Extraction [51.27558374091491]
We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction.
Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
arXiv Detail & Related papers (2021-09-24T17:37:35Z) - Learning Relation Alignment for Calibrated Cross-modal Retrieval [52.760541762871505]
We propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations.
We present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions mutually via inter-modal alignment.
arXiv Detail & Related papers (2021-05-28T14:25:49Z) - Cross-lingual Entity Alignment with Adversarial Kernel Embedding and
Adversarial Knowledge Translation [35.77482102674059]
Cross-lingual entity alignment often suffers challenges from feature inconsistency to sequence context unawareness.
This paper presents a dual adversarial learning framework for cross-lingual entity alignment, DAEA, with two original contributions.
arXiv Detail & Related papers (2021-04-16T00:57:28Z) - ERICA: Improving Entity and Relation Understanding for Pre-trained
Language Models via Contrastive Learning [97.10875695679499]
We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text.
Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
arXiv Detail & Related papers (2020-12-30T03:35:22Z) - 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) - Cross-lingual Entity Alignment with Incidental Supervision [76.66793175159192]
We propose an incidentally supervised model, JEANS, which jointly represents multilingual KGs and text corpora in a shared embedding scheme.
Experiments on benchmark datasets show that JEANS leads to promising improvement on entity alignment with incidental supervision.
arXiv Detail & Related papers (2020-05-01T01:53:56Z)
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.