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.

Latest papers with no code

Entity Alignment with Unlabeled Dangling Cases

no code yet • 16 Mar 2024

We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled.

Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment

no code yet • 2 Mar 2024

Unfortunately, prior arts have attempted to improve the interaction and fusion of multi-modal information, which have overlooked the influence of modal-specific noise and the usage of labeled and unlabeled data in semi-supervised settings.

Unlocking the Power of Large Language Models for Entity Alignment

no code yet • 23 Feb 2024

To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.

Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment

no code yet • 30 Jan 2024

The final prediction of the equivalent entity is derived from the LLM's output.

FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion

no code yet • 17 Dec 2023

As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing.

Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs

no code yet • 10 Nov 2023

Despite the recent popularity of knowledge graph (KG) related tasks and benchmarks such as KG embeddings, link prediction, entity alignment and evaluation of the reasoning abilities of pretrained language models as KGs, the structure and properties of real KGs are not well studied.

Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion

no code yet • 1 Nov 2023

The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources.

Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

no code yet • 11 Sep 2023

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions.

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

no code yet • 5 Jul 2023

Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity.

Cross-domain Recommender Systems via Multimodal Domain Adaptation

no code yet • 24 Jun 2023

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.