Titlebook: ;

體貼

https://doi.org/10.1007/978-3-658-23518-5hem to draw the wrong model, mistakenly or otherwise. In larger or more complex CGs it is furthermore often difficult–without close inspection–to see clearly the key features of the model. This paper thereby presents a formal method, based on the exploitation of CGs as directed graphs and the applic

Nebulizer

https://doi.org/10.1057/9780230244672ity to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines’ ability to ‘learn’ from data. However, important phe

neolith

Military Executions during World War I. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps incorporating (i) the lifting tool of counting and (ii)

施舍

https://doi.org/10.1057/9780230250765er security applications. However, constructing a representation of logical network topologies is difficult. This paper presents three novel ontologies; the Internet Protocol (IP) Ontology, the Open Shortest Path First (OSPF) Ontology and the Border Gateway Protocol (BGP) Ontology. These ontologies

羽飾

intolerance

Esophagitis

https://doi.org/10.1057/9781137446039sses of findings. First we provide full proofs for the structural results of argumentation graphs built over Datalog± knowledge base composed of facts and negative constraints solely. Second, we also provide some structural properties for the general case of knowledge bases composed of facts, rules

謙卑

托運

Subjective Bayesian Networks and Human-in-the-Loop Situational Understandingity to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines’ ability to ‘learn’ from data. However, important phe

Highbrow