Hot-Flash
发表于 2025-3-28 16:54:03
https://doi.org/10.1007/b118340l and its operational definitions support querying a data source containing different levels of uncertainty metadata. Finally, we discuss the perspectives with a view on supporting reasoning over uncertain linked data.
红润
发表于 2025-3-28 19:42:48
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Liability
发表于 2025-3-29 02:23:30
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灿烂
发表于 2025-3-29 03:14:08
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Anal-Canal
发表于 2025-3-29 09:19:57
Ontology-Informed Lattice Reduction Using the Discrimination Power Indexs existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.
旧式步枪
发表于 2025-3-29 14:48:00
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Crepitus
发表于 2025-3-29 17:05:24
https://doi.org/10.1007/978-3-030-04885-3r a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT. to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJT. to efficiently answer assignment queries for a given number of time steps.
赔偿
发表于 2025-3-29 20:05:13
https://doi.org/10.1007/978-3-319-15382-7implication. In most cases, combinations of t-norms and implications do not fit human intuitions. Based on these methods, we suggest the use of the product t-norm in the compositional rule of inference. We combine this t-norm with different known implications. We then study these combinations and check if they give reasonable consequences.
撕裂皮肉
发表于 2025-3-30 00:04:52
https://doi.org/10.1007/978-1-4615-2718-3reasoning capabilities of . with a practical scenario from the digital humanity domain. We chose the FDS project in virtue of its inherent contextual nature, as well as its notable complexity which allow us to highlight many issues connected with contextual knowledge representation and reasoning.
厌恶
发表于 2025-3-30 07:30:10
https://doi.org/10.1007/978-3-642-12331-3e concept lattice as well as distribution of objects on it. Finally, we overcome computational challenges for computing the relative relevance through an approximation approach based on information entropy.