Knowing the No-match: Entity Alignment with Dangling Cases
- URL: http://arxiv.org/abs/2106.02248v1
- Date: Fri, 4 Jun 2021 04:28:36 GMT
- Title: Knowing the No-match: Entity Alignment with Dangling Cases
- Authors: Zequn Sun, Muhao Chen, Wei Hu
- Abstract summary: This paper studies a new problem setting of entity alignment for knowledge graphs (KGs)
Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities.
We construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection.
- Score: 22.909706377522614
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper studies a new problem setting of entity alignment for knowledge
graphs (KGs). Since KGs possess different sets of entities, there could be
entities that cannot find alignment across them, leading to the problem of
dangling entities. As the first attempt to this problem, we construct a new
dataset and design a multi-task learning framework for both entity alignment
and dangling entity detection. The framework can opt to abstain from predicting
alignment for the detected dangling entities. We propose three techniques for
dangling entity detection that are based on the distribution of
nearest-neighbor distances, i.e., nearest neighbor classification, marginal
ranking and background ranking. After detecting and removing dangling entities,
an incorporated entity alignment model in our framework can provide more robust
alignment for remaining entities. Comprehensive experiments and analyses
demonstrate the effectiveness of our framework. We further discover that the
dangling entity detection module can, in turn, improve alignment learning and
the final performance. The contributed resource is publicly available to foster
further research.
Related papers
- 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) - Entity Alignment with Unlabeled Dangling Cases [49.86384156476041]
We propose a novel GNN-based dangling detection and entity alignment framework.
While the two tasks share the same GNN, the detected dangling entities are removed in the alignment.
Our framework is featured by a designed entity and relation attention mechanism for selective neighborhood aggregation in representation learning.
arXiv Detail & Related papers (2024-03-16T17:21:58Z) - EventEA: Benchmarking Entity Alignment for Event-centric Knowledge
Graphs [17.27027602556303]
We show that the progress made in the past was due to biased and unchallenging evaluation.
We construct a new dataset with heterogeneous relations and attributes based on event-centric KGs.
As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment.
arXiv Detail & Related papers (2022-11-05T05:34:21Z) - Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs [22.88552158340435]
We propose and dive into a realistic yet unexplored setting, referred to as continual entity alignment.
It reconstructs an entity's representation based on entity adjacency, enabling it to generate embeddings for new entities quickly.
It selects and replays partial pre-aligned entity pairs to train only parts of KGs while extracting trustworthy alignment for knowledge augmentation.
arXiv Detail & Related papers (2022-07-23T06:52:44Z) - Dangling-Aware Entity Alignment with Mixed High-Order Proximities [65.53948800594802]
dangling-aware entity alignment is an underexplored but important problem in knowledge graphs.
We propose a framework using mixed high-order proximities on dangling-aware entity alignment.
Our framework more precisely detects dangling entities, and better aligns matchable entities.
arXiv Detail & Related papers (2022-05-05T02:39:55Z) - Towards Entity Alignment in the Open World: An Unsupervised Approach [29.337157862514204]
It is a pivotal step for integrating knowledge graphs (KGs) to increase knowledge coverage and quality.
State-of-the-art solutions tend to rely on labeled data for model training.
We offer an unsupervised framework that performs entity alignment in the open world.
arXiv Detail & Related papers (2021-01-26T03:10:24Z) - 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) - Neighborhood Matching Network for Entity Alignment [71.24217694278616]
Neighborhood Matching Network (NMN) is a novel entity alignment framework.
NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference.
It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity.
It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair.
arXiv Detail & Related papers (2020-05-12T08:26:15Z) - 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.