Knowledge-rich self-supervised entity linking

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August undefined, 2024

WebDec 15, 2024 · Knowledge-Rich Self-Supervision for Biomedical Entity Linking Sheng Zhang, Hao Cheng, +6 authors Hoifung Poon Published in Conference on Empirical… 15 December 2024 Computer Science Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. WebJan 17, 2024 · 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 based on instance discrimination and contrastive loss, and suffers from …

microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL · Hugging …

WebMar 2, 2024 · Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label supervision has been considered necessary for accurate alignments. WebApr 11, 2024 · Contrary to previous knowledge graphs, MMKG contains both numerical features and images for all entities as well as entity alignments between pairs of knowledge graphs, which is specially designed for tackling link … data di nascita inter

Entity Linking and Discovery via Arborescence-based Supervised …

Web3 Knowledge-Rich Self-Supervision for Entity Linking Entity linking grounds textual mentions to unique entities in a given database/dictionary. Formally, the goal of entity linking is to learn a function Link : (m;T) !ethat maps mention min the context Tto the unique entity e. Self-supervised en-tity linking assumes no access to any gold ... WebDec 15, 2024 · 12/15/21 - Entity linking faces significant challenges, such as prolific variations and prevalent ambiguities, especially in high-value domai... Web1.3k members in the mlscaling community. ML/AI/DL research on approaches using extremely large models, datasets, or compute to reach SOTA performance marta dessimoz

Knowledge-Rich Self-Supervised Entity Linking DeepAI

Interactive Contrastive Learning for Self-Supervised Entity …

WebThis paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion, question an-swering, and more. Unlike other methods that harvest self-supervision signals based WebSep 2, 2024 · We evaluate our approach on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset, and show significant improvements in performance for both entity linking and discovery compared to identically parameterized models. marta diarteWebDec 15, 2024 · In this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for entity linking, by leveraging readily available domain knowledge. In training, it generates self … marta diener

"WebIn this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for entity linking by leverag-ing readily available domain knowledge to compen-sate for the lack of labeled … " - Knowledge-rich self-supervised entity linking

Knowledge-rich self-supervised entity linking

Shikhar Vashishth Publications - GitHub Pages

WebSelf-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) self-supervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and ... WebIn this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self …

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WebIn this paper, we explore Knowledge-RIch Self-Supervision (K R I S S) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self … WebJan 17, 2024 · Interactive Contrastive Learning for Self-supervised Entity Alignment Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Juanzi Li, Ling Feng Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments.

WebApr 25, 2024 · Heiko Paulheim. 2024. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web 8, 3 (2024), 489–508. Google Scholar; Shichao Pei, Lu Yu, Robert Hoehndorf, and Xiangliang Zhang. 2024. Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference. In WWW. 3130–3136. … WebJun 22, 2024 · We propose a self-supervised embedding learning framework—SelfLinKG—to link concepts in heterogeneous knowledge graphs. Without any labeled data, SelfLinKG …

WebApr 12, 2024 · PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes Ruoyu Wang · Zehao Yu · Shenghua Gao Self-supervised Super-plane for Neural 3D Reconstruction Botao Ye · Sifei Liu · Xueting Li · Ming-Hsuan Yang NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization WebMedical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of …

WebIn this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised ...

WebJun 26, 2024 · Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas Traditional entity linking systems assume that the schema of the knowledge base that ties the predicted entities together is known. They proposed a new method to convert the schema of unknown entities to BERT embedding using attributes and auxiliary tokens. marta di fortiWebKnowledge-Rich Self-Supervised Entity Linking. [ Paper] Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao and … marta dimetrosWebEntity Linking in Tabular Data Needs the Right Attention. no code yet • 5 Jul 2024. We achieve constant memory usage by introducing a Tabular Entity Linking Lite model (TELL … marta del castillo\u0027sWebKnowledge-Rich Self-Supervision for Biomedical Entity Linking EMNLP 2024 Findings Sheng Zhang* , Hao Cheng * , Shikhar Vashishth * , Cliff Wong , Jinfeng Xiao , Xiaodong Liu , Tristan Naumann , Jianfeng Gao , Hoifung Poon (*equal contribution) [ Code ] Modular Self-Supervision for Document-Level Relation Extraction EMNLP 2024 marta dettoriWebApr 14, 2024 · Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is not satisfactory under the ... marta di forti cannabisWebApr 25, 2024 · Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over … marta di mauroWebDec 15, 2024 · Knowledge-Rich Self-Supervised Entity Linking Authors: Sheng Zhang Hao Cheng Shikhar Vashishth Cliff Wong Abstract Entity linking faces significant challenges, … data di partenza