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Found 28 papers, 23 papers with code
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks.
Generating Explanations to Understand and Repair Embedding-based Entity Alignment
In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results.
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models.
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs
We pre-train a large teacher KG embedding model over linked multi-source KGs and distill knowledge to train a student model for a task-specific KG.
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies.
Deep Active Alignment of Knowledge Graph Entities and Schemata
With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner.
Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs
We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch.
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs.
Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives.
Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities
We propose a walk-based inductive reasoning model to tackle the new setting.
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
To this end, we propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complementarity of graph structures and text information.
Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs
To avoid retraining an entire model on the whole KGs whenever new entities and triples come, we present a continual alignment method for this task.
$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
It is useful for a thorough comparison and analysis of various embedding models and tasks.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
Ensemble Semi-supervised Entity Alignment via Cycle-teaching
We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher.
Informed Multi-context Entity Alignment
Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources.
Principled Representation Learning for Entity Alignment
Embedding-based entity alignment (EEA) has recently received great attention.
Knowing the No-match: Entity Alignment with Dangling Cases
Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities.
Ranked #1 on
Entity Alignment
on DBP2.0 zh-en
Towards Principled Representation Learning for Entity Alignment
In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.
Distributed Representations of Entities in Open-World Knowledge Graphs
DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings.
Knowledge Association with Hyperbolic Knowledge Graph Embeddings
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations.
Ranked #28 on
Entity Alignment
on DBP15k zh-en
TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings.
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings.
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures.
Ranked #29 on
Entity Alignment
on DBP15k zh-en
Multi-view Knowledge Graph Embedding for Entity Alignment
Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs.
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding.
Recurrent Skipping Networks for Entity Alignment
We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA).
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.
