Deep learning system performs reasoning on RDF graphs with high precision and recall.
problem Scalability and robustness issues in deductive reasoning over large RDF graphs.
method Trained a deep learning system on RDF knowledge graphs to perform reasoning.
result Deep learning system achieves high precision and recall compared to deductive methods.
Tensor-based embeddings improve knowledge graph fact prediction.
problem Predicting new facts in knowledge graphs.
method Knowledge-Enriched Tensor Factorization
result 5% to 50% relative improvement over state-of-the-art techniques.
System exposes study population descriptions in clinical guidelines.
problem Challenges in understanding applicability of clinical guidelines.
method Developed an ontology-enabled prototype system using SIO.
result Allows medical practitioners to better understand study populations.
A new framework for knowledge graph embedding using sheaves.
problem Learning representations for entities and relations in knowledge graphs.
method Using cellular sheaves to describe knowledge graph embeddings with consistency constraints.
result A generalized framework for reasoning about knowledge graph embedding models.
New attacks manipulate knowledge graph embeddings by adding or deleting facts.
problem Lack of robustness of KGE to adversarial attacks.
method Data poisoning attack strategies to manipulate knowledge graph embeddings.
result Demonstrated effectiveness and efficiency of proposed attack strategies.
Method finds motifs in knowledge graphs, revealing their structure.
problem Identifying meaningful subunits in knowledge graphs.
method Inspired by simple graphs, the approach uses compression techniques to find motifs.
result Motifs found reflect the basic structure of the graph.
Paper proposes KE-GCN for better graph embedding.
problem Efficiently leveraging complex graphs with heterogeneous entities and relations.
method Combines GCNs and knowledge embedding methods.
result Advantages in knowledge graph alignment and entity classification.
KGNN-LS improves recommender systems using knowledge graphs and label smoothness.
problem Improving recommender systems through better user-item embeddings.
method KGNN-LS combines knowledge graphs, user-specific embeddings, and label smoothness regularization.
result KGNN-LS outperforms state-of-the-art baselines and handles cold-start scenarios.
Proposes a new method for predicting missing relations in knowledge graphs.
problem Predicting missing relations between entities in knowledge graphs.
method Relational message passing method considering only edge features without entity IDs.
result PathCon method outperforms state-of-the-art methods significantly.
Bayes EMbedding refines embeddings from knowledge and behavior graphs.
problem Combining knowledge and behavior graphs for better entity embeddings.
method Bayesian framework integrating pre-trained embeddings from both graphs.
result BEM refines embeddings from both knowledge and behavior graphs.
ZSL-KG learns class representations from common sense knowledge graphs.
problem Predicting classes without labeled examples using semantic class representations.
method TrGCN, a novel transformer graph convolutional network, embeds nodes from common sense knowledge graphs in a vector space.
result ZSL-KG improves over existing methods on five out of six zero-shot benchmark datasets.
This study evaluates a health knowledge graph for robustness in EHRs.
problem Evaluate robustness of a health knowledge graph in EHRs.
method Analyze sample size, unmeasured confounders, and non-linear functions.
result Identify sample size and unmeasured confounders as major sources of error.
Paper investigates neural query graph ranking for complex question answering over knowledge graphs.
problem Improving neural models for complex question answering over knowledge graphs.
method Experimented with six ranking models, proposed a self-attention based slot matching model.
result Proposed model outperforms other models on DBpedia QA datasets.
This work proposes a hybrid method for error detection in noisy Knowledge Graphs.
problem Error detection in noisy Knowledge Graphs.
method Hybrid and modular approach combining path ranking and representation learning.
result Hybrid method outperforms individual methods on benchmarks and real-world dataset.
This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.
problem Evaluating the robustness of neural embeddings in changing knowledge graphs.
method Proposes an open-source evaluation pipeline using relation-centric connectivity measures.
result Demonstrates the importance of simulating embedding accuracy for frequently updated knowledge graphs.
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connecte…
HAKE embeds entities in polar coordinates to model semantic hierarchies in knowledge graphs.
problem Lack of modeling semantic hierarchies in knowledge graph embeddings.
method HAKE embeds entities in a polar coordinate system, where the radial coordinate represents hierarchy levels and the angular coordinate distinguishes entities at the same level.
result HAKE significantly outperforms existing methods on link prediction tasks in knowledge graphs.
pLogicNet combines logic rules and embeddings for efficient knowledge graph reasoning.
problem Efficiently predicting missing facts in knowledge graphs.
method Combines Markov Logic Networks with knowledge graph embeddings using variational EM algorithm.
result pLogicNet outperforms traditional methods on multiple knowledge graphs.
Improved GCNNs tackle noisy knowledge graphs for better link prediction.
problem Link prediction on noisy biomedical knowledge graphs.
method Regularized attention mechanism for GCNNs, interpretable visualization methods.
result Improved performance on noisy datasets, automated denoising potential.
RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.
problem Interpreting GNN predictions for link prediction in heterogeneous settings is challenging.
method RAW-Explainer uses random walk objective and neural network to generate connected, concise subgraph explanations.
result RAW-Explainer strikes a balance between explanation quality and computational efficiency.
Paper proposes unsupervised knowledge graph alignment with adversarial learning.
problem Aligning knowledge graphs from different sources or languages without large amounts of aligned triplets.
method Adversarial learning framework to align entity and relation embeddings, with mutual information regularization.
result Framework effectively aligns knowledge graphs in unsupervised and weakly-supervised settings.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Pykg2vec simplifies knowledge graph embedding research.
problem Learning representations of entities and relations in knowledge graphs.
method Flexible and modular software architecture implementing 16 state-of-the-art algorithms.
result Accelerates research in knowledge graph representation learning.
Improved knowledge graph embedding using taxonomic information.
problem Learning about domains in knowledge graphs using embedding models.
method Minimal modifications to existing knowledge graph completion methods to incorporate taxonomic information.
result Our model is fully expressive, respecting subclass and subproperty information.
This paper defines resilience in knowledge graph embeddings and surveys existing works.
problem Challenges in knowledge graph embedding models, including noise, missing information, distribution shift, and adversarial attacks.
method Unified definition of resilience, formalization in the context of knowledge graphs, systematic survey of existing works.
result Most existing works focus on robustness, leaving other aspects of resilience unexplored.
The study categorizes knowledge graph relations and explains their embedding representations.
problem Understanding how knowledge graph relation representations capture semantic information.
method Categorizing knowledge graph relations into three types and deriving explicit requirements for their representations.
result Empirical properties of relation representations and the performance of methods are justified by the analysis.
Graph few-shot learning improves node classification with prior knowledge transfer.
problem Challenging semi-supervised node classification with few labeled nodes.
method Incorporates prior knowledge from auxiliary graphs into a transferable metric space.
result Improves classification accuracy on target graphs.
Enhances knowledge graph completion with mixed geometry tensor factorization.
problem Capturing nuanced distributional properties in knowledge graphs.
method Combines Euclidean and hyperbolic geometries for tensor factorization.
result Improves link prediction accuracy with fewer parameters.
Study restricts causal graphs with expert knowledge.
problem Restricting causal graphs to include expert orientation knowledge.
method Prove properties, present new orientation rules, develop algorithms.
result Shows how to uniquely represent restricted essential ancestral graphs.
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
problem Insufficient performance of existing temporal models on extrapolated queries and time prediction.
method Introduced an extension to current temporal models using relative temporal information.
result Improved performance on extrapolated queries and time prediction.
A novel fact-checking method using debate dynamics on knowledge graphs.
problem Fact-checking on knowledge graphs with user comprehension and interactive reasoning.
method Reinforcement learning agents debate on paths in the graph to classify facts as true or false.
result Interactive reasoning and user understanding of AI decisions on knowledge graphs.
Unified framework for learning from unlabeled and labeled data in knowledge graphs.
problem Scarcity of relation-specific labeled triples and ad hoc scoring functions limit model training and generalizability.
method Two-stage procedure: unsupervised pretraining over heterogeneous corpora followed by supervised learning with multiple relation types.
result Established a nonasymptotic risk bound quantifying the benefit of large-scale unlabeled data.
Graphs evolve; GNNs learn from past tasks.
problem Learning from evolving graphs and new classes.
method Incremental training method for lifelong learning on graphs.
result Less than 50% of the GNN's receptive field is needed for 95% accuracy.
TuckER predicts missing facts in knowledge graphs using tensor decomposition.
problem Predicting missing facts in knowledge graphs.
method TuckER uses Tucker decomposition of binary tensor representations.
result TuckER outperforms state-of-the-art models in link prediction.
Quaternion embeddings model entities and relations in knowledge graphs.
problem Modeling latent inter-dependencies and expressive rotations in knowledge graphs.
method Quaternion-valued embeddings and rotations in hypercomplex space.
result Quaternion embeddings achieve state-of-the-art performance on knowledge graph benchmarks.
MKR uses knowledge graphs to enhance recommendation, improving performance across various categories.
problem Collaborative filtering's sparsity and cold start issues in real-world recommendation scenarios.
method MKR is a deep end-to-end framework that leverages knowledge graph embedding for recommendation, associating two tasks through cross&compress units.
result MKR achieves substantial gains in movie, book, music, and news recommendation, outperforming state-of-the-art baselines.
A method to reduce knowledge graph embedding models by binarizing parameters.
problem Large memory requirements for tensor factorization models in knowledge graph completion.
method Introducing a quantization function to binarize parameters of CP tensor decomposition.
result Successfully reduced model size by more than an order of magnitude while maintaining task performance.
PyKEEN 1.0 simplifies KGE model creation and optimization.
problem Training and evaluating knowledge graph embeddings (KGEs).
method Composes KGEMs with various interaction models, training approaches, and loss functions. Implements automatic memory optimization and extensive HPO functionalities.
result PyKEEN 1.0 streamlines KGE model creation and optimization.
The paper extends graph embedding models to handle multiple relations.
problem Link prediction in multi-relational networks.
method Generalized pseudo-Riemannian embedding models to multi-relational networks, considering relations as submanifolds.
result Validation of the approach in link prediction tasks, including knowledge graph completion and biological domain analysis.
AdaGCN uses AdaBoost to efficiently integrate high-order neighbor knowledge in graph neural networks.
problem Efficiently exploring and exploiting knowledge from different hops of neighbors in graph neural networks.
method Incorporates AdaBoost into graph convolutional networks to integrate knowledge from high-order neighbors.
result AdaGCN achieves state-of-the-art prediction performance across different graphs and label rates.
Improved RL for knowledge graph reasoning with entity types.
problem Challenges in path-based relational reasoning over knowledge graphs.
method Type-enhanced RL agent using GNN for neighborhood information.
result Outperforms state-of-the-art RL methods and discovers novel paths.
KEEN Universe provides reproducible and transferable knowledge graph embeddings.
problem Lack of reproducibility and transferability in KGE experiments.
method Developed an ecosystem with Python packages for reproducible and transferable KGEs.
result KEEN Universe facilitates sharing of trained KGE models across different fields.
RotatE embeds knowledge graphs using rotations in complex space for better link prediction.
problem Predicting missing links in knowledge graphs.
method RotatE models relations as rotations in complex vector space, using self-adversarial negative sampling.
result RotatE models and infers various relation patterns, outperforming existing models.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Graph representation learning improves with domain knowledge.
problem Efficiently learning graph representations from scarce labels.
method Multi-task knowledge distillation combining graph metrics.
result Improves prediction performance, especially with limited training data.
Efficiently tunes hyperparameters for knowledge graph embeddings.
problem Hyperparameter tuning is challenging and costly for knowledge graph embeddings.
method Augmented probabilistic framework with variational expectation-maximization.
result Achieved state-of-the-art performance in link prediction.