Jan 6, 2022 · The proposed Causal Knowledge Graph (CausalKG) framework, leverages recent progress of causality and KG towards explainability. CausalKG intends to address the lack of a domain adaptable causal model and represent the complex causal relations using the hyper-relational graph representation in the KG.

Mar 1, 2023 · A framework to build a causal knowledge graph for chronic diseases and cancers by discovering semantic associations from biomedical literature

Causal Knowledge Graph Framework consists of three main steps, i) a Causal Bayesian Network and a domain-specific observational dataset, ii) Causal Ontology creation and enriching the domain ontology with causal relationships, and iii)

Jul 21, 2023 · We further propose a Causality-enhanced knowledge graph Embedding (CausE) framework. CausE employs causal intervention to estimate the causal effect of the confounder embeddings and design new training objectives to make stable predictions.

Oct 28, 2023 · We propose a new learning paradigm for KGE in the context of causality and design a Causality-enhanced knowledge graph Embedding (CausE for short) framework to learn causal embeddings for KGE models. We conduct comprehensive experiments on public benchmarks to demonstrate the effectiveness of CausE.

Apr 23, 2023 · To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies.

The human mind has an innate understanding of causality.15 It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios.8 The unseen and unknown scenarios are called “counterfactuals.”2

Jan 6, 2022 · Causal Knowledge Graph Framework consists of three main steps, i) a Causal Bayesian Network and a domain-specific observational dataset, ii) Causal Ontology creation and enriching the domain...

Objective: The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data.

5 days ago · In our study, we introduce a novel framework leveraging LLMs’ decent ability to detect and infer causal relations to construct a causal Knowledge Graph (KG) which represents knowledge that the LLM recognizes.