[ad_1] Introduction Model checking is a formal verification technique that has gained significant popularity in recent years for verifying the correctness of systems. It involves the automatic verification of system properties against a formal model…
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[ad_1] Introduction: Answer set programming (ASP) is a declarative programming paradigm that has gained popularity in recent years for solving complex computational problems. Unlike traditional imperative programming languages, ASP allows users to specify the desired…
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[ad_1] Introduction Probabilistic logic programming is a powerful framework that combines the expressiveness of logic programming with the ability to reason under uncertainty. This approach has been widely used in various fields such as artificial…
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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 In the era of big data, one of the major challenges faced by researchers and organizations is the interoperability of knowledge across different domains and systems. Linked data has emerged as a powerful…
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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 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 In recent years, there has been an increasing interest in graph neural networks, which are powerful tools for learning representations of graph-structured data. Graph attention networks, a type of graph neural network, have…
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