Abstract
A major challenge in cyber-physical environments is the increasing
heterogeneity that complexifies access to devices. This challenge can be
addressed by hingin, a linked-data compatible property-graph-based
platform promoted by Orange. Besides being able to represent cyber-physical
environments at a system-level, hingin can provide uniform access to
heterogeneous devices in them. However, a necessary condition for hingin
to satisfy this goal is to have a description of these devices.
Nevertheless, the heterogeneity related to devices are well present in
their descriptions and mainly include syntactic and semantic heterogeneity
that occurs as a result of the varying data formats and vocabularies
respectively. Consequently, inputting these descriptions in hingin
remains a challenge. To tackle this challenge, standards and technologies
that were originally conceived for the Semantic Web can be used. More
specifically, RDF may be used to handle syntactic heterogeneity by acting
as a lingua franca as it is independent of data formats.
Also, it may be used to resolve semantic heterogeneity by using vocabularies
and ontologies to eliminate the ambiguity of terms as it fixes their interpretations.
However, transforming the existing description of objects to RDF is again
challenging. Mapping languages can be used to encode the transformation.
However, their usage is complex even with the intervention of human experts
as it involves manually considering data elements from the device
description and looking for ontology terms to which they can be mapped.
Thus, in this work, our aim is to provide a semi-automatic and generic
approach to facilitate the generation of RDF from heterogeneous device
description. We chose a semi-automatic approach to compensate for the lack
of semantics in device description and potential imprecision in the final
transformation. Moreover, our approach is generic in that it is independent
of hingin and can interoperate with any other platform via its RESTful
API.
Our approach takes as input the raw description extracted from sources
such as the object manuals, keywords that describe schema elements in the
latter description and a set of ontologies. It outputs possible mappings to
transform the object description to RDF. To generate these mapping rules,
the approach first identifies ontology entities that can be used to model
individual schema elements from the input data by calculating a similarity
score between them. If the score exceeds a certain threshold specified by
the human expert, an ontology entity is considered a suitable mapping for a
schema element. Using the latter mappings, the mappings rules are
generated. Finally, the human expert chooses, modify and refine one of the
mappings that are finally used to transform the original data to RDF.
#### Références
https://drive.google.com/open?id=1FmgmwrIJKi9xGvDjmjU3sosohuatb4_G