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
Most implemented papers
Aligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
Valentyn1997/kg-alignment-lessons-learned
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In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
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.
Collective Entity Alignment via Adaptive Features
DexterZeng/CEA
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Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration.
Active Learning for Entity Alignment
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph
MaoXinn/MRAEA
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The International Conference on Web Search and Data Mining (WSDM) 2020
To tackle these challenges, we propose a novel Meta Relation Aware Entity Alignment (MRAEA) to directly model cross-lingual entity embeddings by attending over the node's incoming and outgoing neighbors and its connected relations' meta semantics.
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
mberr/rank-based-evaluation
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In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
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.
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.
