=Paper=
{{Paper
|id=Vol-3890/paper-36
|storemode=property
|title=Nanosafety data made interoperable using semantic modeling and linked-data knowledge
                                       graphs
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-36.pdf
|volume=Vol-3890
}}
==Nanosafety data made interoperable using semantic modeling and linked-data knowledge
                                       graphs==
<pdf width="1500px">https://ceur-ws.org/Vol-3890/paper-36.pdf</pdf>
<pre>
Nanosafety data made interoperable using semantic
modeling and linked-data knowledge graphs
Ammar Ammar1,∗ , Egon Willighagen1
1
    Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands


                  Abstract
                  Achieving data interoperability is a critical challenge in the increasingly complex landscape of the nanosafety
                  field, where ensuring the safe use of nanomaterials is of great importance. The unique properties of nanoma-
                  terials, stemming from their size and structure, necessitate comprehensive and standardized data to evaluate
                  potential risks and hazards. One significant challenge lies in the diversity of experimental approaches, measure-
                  ment techniques and exchange formats employed in nanosafety research [1]. Fortunately, semantic modeling
                  coupled with linked-data knowledge graphs emerges as a powerful solution. Semantic modeling involves
                  structuring data in a way that adds meaning and context to the information, facilitating better harmonization
                  and standardization. Linked-data knowledge graphs take this a step further by establishing relationships be-
                  tween diverse datasets and their metadata, creating a web of interconnected information. That allows for better
                  understanding and seamless data integration and exchange across different domains and applications. Moreover,
                  the semantic approach inherently complies with the FAIR principles (Findable, Accessible, Interoperable and
                  Reusable) [2] and covers several of its sub-principles. Thus, making the data more accessible and reusable for
                  the community.
                      The semantic model presented in this work adopts several standardized ontologies to describe both the
                  datasets and their metadata. For metadata representation, DCAT [3] and VoID [4] ontologies were used.
                  Moreover, more specialized ontologies were used to represent nanosafety data, namely, NPO [5] for nanomaterial
                  entities and BAO [6] and eNanoMapper [7] ontologies to represent the bioassays, experimental conditions and
                  measured outputs. The model captures two types of assays, toxicity assays and gene expression assays allowing
                  to reveal insights from gene expression signatures at concentrations where a nanomaterial is deemed toxic.
                      This approach utilizes the RDF Mapping Language (RML) [8] and its extension (YARRML) [9] to represent
                  the semantic model as a set of reusable mapping rules to convert related datasets into a knowledge graph. Then,
                  the knowledge graph can be explored using SPARQL query language. For example, finding relations between
                  up/down-regulated genes and toxicity levels of a nanomaterial in a specific cell line. Another example, enriching
                  the nanomaterials with information about the key events in adverse outcome pathways where they take effect.
                  This can be done using federated queries against external sources like AOP-Wiki [10] and ENM-MIE [10]
                  knowledge graphs. Furthermore, this knowledge representation allows morphing the data model into another
                  one that adheres to different ontologies or vocabularies [11]. For example, using SPARQL, the nanomaterial
                  data can be remapped to a Schema.org compliant model which then can be used to annotate relevant web pages
                  with semantic metadata.
                      In summary, making data interoperable through semantic modeling and linked-data knowledge graphs is es-
                  sential for advancing our understanding of nanomaterials’ safety profiles. This approach not only enhances data
                  FAIRness but also promotes seamless integration and exchange of information, fostering a more interconnected
                  ecosystem for diverse applications.

                  Keywords
                  Semantic Modeling, Knowledge Graph, Linked Data, Nanosafety Data, FAIR, Interoperability,




SWAT4HCLS, February 26–29, 2024, Leiden, The Netherlands
Envelope-Open a.ammar@maastrichtuniversity.nl (A. Ammar); egon.willighagen@maastrichtuniversity.nl (E. Willighagen)
Orcid            0000-0002-8399-8990 (A. Ammar); 0000-0001-7542-0286 (E. Willighagen)
                                       © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)

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