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Found 16 papers, 12 papers with code
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism
Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion
Temporal Knowledge Graph Completion
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph.
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives.
Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG.
An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation
Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion.
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs.
Ranked #1 on
Entity Alignment
on DBP1M DE-EN
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs, which is a crucial step for integrating KGs.
Ranked #4 on
Entity Alignment
on dbp15k fr-en
(using extra training data)
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs.
Ranked #6 on
Entity Alignment
on dbp15k ja-en
(using extra training data)
Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA).
Ranked #2 on
Entity Alignment
on YAGO-WIKI50K
Relational Reflection Entity Alignment
Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs (KGs), which is essential in integrating multi-source KGs.
Ranked #3 on
Entity Alignment
on DICEWS-1K
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph
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.
Ranked #17 on
Entity Alignment
on DBP15k zh-en
Is Discriminator a Good Feature Extractor?
From this perspective and combined with further analyses, we found that to avoid mode collapse, the features extracted by the discriminator are not guided to be different for the real samples, but divergence without noise is indeed allowed and occupies a large proportion of the feature space.
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
This paper describes our submissions to Task 2 in SemEval 2018, i. e., Multilingual Emoji Prediction.
