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Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases

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Hang Yin, Liyao Xiang, Dong Ding, Yuheng He, Yihan Wu, Xinbing Wang, Chenghu Zhou

Computation and Language · Submitted 16 Mar 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.

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arXiv ID2403.10978 (version 2)
Pith IDC3SHOTJ2, Pith's permanent record of this paper and its review
CitationChenghu Zhou, Dong Ding, Hang Yin, Liyao Xiang, Xinbing Wang, Yihan Wu, Yuheng He. "Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases." arXiv:2403.10978 (2024). https://pith.science/paper/C3SHOTJ2
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,699 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.15 · human participants 0.02 · controlled comparison 0.95 · randomized assignment 0.02 · states quantitative result 0.84 · compares with prior results 0.98 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then 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. iPULE offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. background 0.99 0.15 0.03 asserted 0.93
s1 To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then entity alignment. contribution 0.90 0.83 0.06 asserted 0.99
s2 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. iPULE offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. method 0.97 0.90 0.07 asserted 1.00
s3 Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training. result 1.00 0.96 0.77 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then 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. iPULE offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.

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