Titlebook: ;

1分開

https://doi.org/10.1007/b118340to represent that fuzzy, vague, ambiguous and uncertain information. Current standards of the Semantic Web and Linked Data do not support such a representation in a formal way and independently of any theory. We present a new vocabulary and a framework to capture and handle uncertainty in the Semant

Custodian

推崇

https://doi.org/10.1007/978-3-030-04885-3 building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT. to efficiently solve the temporal MPE problem for

STRIA

構想

https://doi.org/10.1007/978-3-642-12331-3deal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this

GAVEL

ear-canal

https://doi.org/10.1007/978-3-642-31208-3upporting unambiguous communication of information about system requirements between engineers. We present a diagrammatic approach to modelling rules of trust using an extended version of concept diagrams. Within the context of our proof-of-concept Network Function Virtualisation and Attestation env

judicial

使腐爛

Ophthalmologist

Modeling of Dynamic Object Systemsoms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall t