[ad_1] Introduction Causal inference is a crucial aspect of research that helps us understand the relationships between variables and determine cause-and-effect relationships. By identifying the causal relationships between variables, researchers can make informed decisions and…
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[ad_1] Introduction Analogical reasoning has been recognized as a powerful cognitive process that enables individuals to transfer knowledge from one domain to another. This mechanism allows us to draw parallels between similar situations and apply…
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[ad_1] Introduction Commonsense reasoning is a fundamental aspect of human cognition that allows individuals to make inferences, draw conclusions, and navigate the complexities of everyday life based on a set of shared beliefs and assumptions.…
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[ad_1] Introduction Knowledge base completion is a fundamental task in the field of knowledge representation and reasoning. It aims to automatically infer missing facts in a knowledge base by leveraging existing knowledge and relationships. The…
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[ad_1] Introduction Semantic web technology has emerged as a promising solution for organizing and extracting knowledge from the vast amount of information available on the internet. By using standardized metadata and ontologies, the semantic web…
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[ad_1] Introduction Ontology learning is a crucial aspect of knowledge engineering that involves the automatic extraction of domain-specific knowledge from unstructured text or data. It plays a vital role in the development of domain models,…
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[ad_1] Introduction: Knowledge graphs have emerged as a powerful tool for representing structured knowledge in a variety of domains. These graphs provide a flexible and scalable way to capture relationships between entities and concepts, making…
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[ad_1] Introduction Graph generative models have gained significant attention in recent years due to their ability to synthesize graph structures with desired properties. These models have various applications in fields such as social network analysis,…
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[ad_1] Introduction Graph Convolutional Networks (GCNs) have gained significant attention in recent years due to their effectiveness in node classification tasks on graph-structured data. GCNs are a type of neural network designed to operate on…
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[ad_1] Introduction: Recurrent relational networks have emerged as a powerful tool for relational reasoning in various domains such as natural language processing, computer vision, and robotics. These networks have the ability to capture complex relationships…
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