Entity Alignment
106 papers with code • 10 benchmarks • 8 datasets
Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
Benchmarks
These leaderboards are used to track progress in Entity Alignment
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DBP15K
UMVM
EventEA
Latest papers with no code
Cross-domain Recommender Systems via Multimodal Domain Adaptation
Several approaches in the literature have been proposed to tackle the problem of data sparsity, among which cross-domain collaborative filtering (CDCF) has gained significant attention in the recent past.
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model.
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity.
Cybersecurity Entity Alignment via Masked Graph Attention Networks
Cybersecurity vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums.
Enriching Wikidata with Linked Open Data
We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation.
Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities
By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities -- that can be far away in the KG -- connected by the same relation.
