arXiv research

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.

Meta-learning model predicts intervention effects from uncertain causal graphs.

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…

Novel algorithms for online learning with uncertain feedback graphs reduce regret.

Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitti…

Probabilistic fair clustering tackles uncertain group membership.

Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…

AGFN improves causal discovery by integrating expert feedback and handling latent confounding.

This paper tackles JSSP with uncertain task durations using DRL.

Unified framework predicts S&P500 index direction using transfer learning and causal graph.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of…

A new framework for knowledge graph embedding using sheaves.

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…

SOLBP extends efficient inference to uncertain Bayesian networks.

Agent learns to navigate uncertain 3D maps using a hybrid planner.

Method finds motifs in knowledge graphs, revealing their structure.

Paper proposes KE-GCN for better graph embedding.

We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background know…

Novel model improves clinical risk prediction by transferring knowledge between tasks over time.

Proposes a new method for predicting missing relations in knowledge graphs.

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its ef…

ZSL-KG learns class representations from common sense knowledge graphs.

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end…

This work proposes a hybrid method for error detection in noisy Knowledge Graphs.

This work benchmarks neural embeddings for link prediction in evolving 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.

RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.

Survey of methods to incorporate external knowledge into stock price prediction.

This paper defines resilience in knowledge graph embeddings and surveys existing works.

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior…

Enhances knowledge graph completion with mixed geometry tensor factorization.

Extracurricular learning closes the accuracy gap in knowledge distillation.

New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.

Study restricts causal graphs with expert knowledge.

Paper proposes AI for stock market forecasting using external knowledge.

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous…

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited …

Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.

We model GitHub interactions as a temporal knowledge graph for software engineering questions.

A decentralized policy achieves logarithmic regret for multi-agent MAB problems with communication constraints.

A novel fact-checking method using debate dynamics on knowledge graphs.

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Me…

Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point…

Graphs evolve; GNNs learn from past tasks.

PyKEEN 1.0 simplifies KGE model creation and optimization.

The paper extends graph embedding models to handle multiple relations.