Introduction to Semantically Enhanced

What is/are Semantically Enhanced?

Semantically Enhanced - The proposed work relates to a novel algorithm that focuses on an object-oriented exploration of unknown environments for aerial robots, able to generate volumetric representations of surroundings, semantically enhanced by labels for each object of interest. ^[1] The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. ^[2] Reporting on an actual real-world system, the paper also provides a roadmap-view of how electronic filing of administrative project data can be semantically enhanced and opened-up to provide the basis for new ways into the data that are key for future breakthrough AI interfaces. ^[3] net/ browse_journals) that are semantically enhanced during their journal production workflow. ^[4] Our approach results in semantically enhanced and low-dimensional representations. ^[5]

Novel Semantically Enhanced

To address these issues, we propose a novel Semantically Enhanced CoAP Gateway (SECoG) for complex service mashups. ^[1] This paper exploits technologies from the semantic Web domain, for the design of a novel semantically enhanced IoT-enabled intelligent control system (SEMIoTICS) architecture. ^[2]

semantically enhanced metadatum

The semantically enhanced metadata records are accessible as standard ISO 19115/19139 XML documents via standard search interfaces. ^[1] The approach relies on an open ecosystem, where dataset owners and providers publish semantically enhanced metadata on their own sites. ^[2]

semantically enhanced cloud

In this context, the current paper presents a semantically enhanced cloud-based IT approach which integrates learning pathways with competence management of personnel working as management and technical employees in a smart city municipality. ^[1] The labels from the semantically enhanced cloud are used to reduce the curbs’ searching area. ^[2]

semantically enhanced classification

In this article, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). ^[1] In this paper, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). ^[2]