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Found 10 papers, 8 papers with code
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
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.
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM).
Real-time Workload Pattern Analysis for Large-scale Cloud Databases
This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis.
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver calculates the probabilistic sampled graph size, a metric that predicts the degree of parallelism in graph sampling.
Unsupervised Entity Alignment for Temporal Knowledge Graphs
State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA.
Ranked #1 on
Entity Alignment
on YAGO-WIKI50K
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
Ranked #2 on
Entity Alignment
on DBP1M DE-EN
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.
