Concept for metadata and time series data integration
      based on a material science application ontology*

                                  Paul Zierep, Dirk Helm

                  Fraunhofer IWM, Wöhlerstraße 11, 79108 Freiburg, Germany
                        paul.zierep@iwm.fraunhofer.de


1       Introduction

    The matching between ontologies and classical datasets allows for an improved in-
terpretability and understanding of the associated information and its interoperability.
However, the integration of large tabular datasets poses various difficulties concerning
the storage and accessibility, since the data cannot be accessed using only the ontology
as an interface.
    Whereas many categorical tabular datasets can be used to automatically populate
ontologies, this approach is technically not feasible for large continuous datasets such
as time series data. Although hybrid query systems such as R2RML and RML [1] allow
for the mapping of relational databases and heterogeneous data formats to Resource
Description Framework (RDF) graphs, these techniques are only suitable for trivial data
schemas [2], which is not the case for our time series datasets.
    In the presented poster, we propose a protocol to include time series data into a ma-
terial science ontology using a two-step approach that combines ontology-based
metadata querying with an additional functionality that allows for the performant re-
trieval of the associated time series data. The protocol demonstrates the first conceptual
prototype of the envisioned system. We are looking forward to fruitful discussions with
the community to further develop the proposed system.


2       Methods

2.1     Ontology development

   To design the mapping concept, we developed a prototype ontology based on the
tensile test experiment. The ontology concepts were derived from experimental da-
tasets, the test standard ISO 6892-1 and interviews with domain experts. All concepts
were classified into two superclasses (MetaData and TimeSeriesData), that provide the
attributes required for the designed data population strategy. The structure of the super-
classes are described in detail in the poster.



*   Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons Li-
    cense Attribution 4.0 International (CC BY 4.0).
2


2.2       Data Integration Pipeline

   To demonstrate data integration and data retrieval in our model system we imple-
mented a basic pipeline. The pipeline was programmed using the ontology-based open-
source Python framework SimPhoNy [3] and its major component OSP core (OSP:
Open Simulation Platform). SimPhoNy allows for the manipulation of Abox individu-
als using CUDS (Common Universal Data Structure) objects.
   Data parsing. To parse the raw datasets we implemented a mapping routine that
reads the dataset and assigns the metadata to corresponding MetaData CUDS objects
as well as each time series column to TimeSeriesData CUDS objects. The TimeSe-
riesData CUDS objects only store the information required to extract the specific col-
umn (e.g. column index, number of header rows and file path). The CUDS objects can
be serialized to ontology individuals in a RDF graph.
   Data retrieval. The data can be queried using the SimPhoNy application program-
ming interface (API) as well as SPARQL queries. The MetaData individuals can be
used to perform complex queries, such as filtering for tensile test experiments with
specific properties (e.g., experiments that used as specific material X with a specimen
width larger than Y). The corresponding TimeSeriesData individuals can be used to
extract the column data using additional data analysis tools.


3         Results and Discussions

   The implemented workflow enables the storing and retrieval of time series data in a
semantically enriched ontology. The designed data parser allows for the parsing and
mapping of semi-structured tabular data. The ontologically stored metadata can be used
for complex queries of the time series data that would be difficult to perform using only
the raw data files.


4         Funding

  This research was funded by the Federal Ministry of Education and Research Ger-
many (BMBF) within the project StahlDigital (funding code: 13XP5116C).


References
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    3. OSP core. SimPhoNy. (2021). https://github.com/simphony/osp-core