=Paper=
{{Paper
|id=Vol-3324/om2022_poster3
|storemode=property
|title=Meta2KG: transforming metadata to knowledge graphs
|pdfUrl=https://ceur-ws.org/Vol-3324/om2022_poster3.pdf
|volume=Vol-3324
|authors=Nora Abdelmageed,Birgitta König-Ries
|dblpUrl=https://dblp.org/rec/conf/semweb/AbdelmageedK22
}}
==Meta2KG: transforming metadata to knowledge graphs==
https://ceur-ws.org/Vol-3324/om2022_poster3.pdf
Meta2KG: Transforming Metadata to Knowledge
Graphs
Nora Abdelmageed1,2,3 , Birgitta König-Ries1,2,3
1
Heinz Nixdorf Chair for Distributed Information Systems
2
Michael Stifel Center Jena
3
Friedrich Schiller University Jena, Jena, Germany
Abstract
Metadata is used to describe data. It includes information about the who, when, where, how, and why of
data collection. Ideally, it should be in a machine-understandable format like RDF. This enables queries
using structured query languages like SPARQL and empowers further data usage. In this paper, we
investigate metadata as a source for generating Knowledge Graphs (KGs). We introduce a fully automatic
approach that transforms raw metadata files into a Knowledge Graph (KG). Our resources and code are
publicly available1 .
Keywords
Metadata Analysis, RDF, Matching, Knowledge Graph, Embeddings
1. Introduction
Knowledge Graphs (KGs) are widely used to represent information about entities of interest
and their relations [1]. Lately, this includes information encoded in scientific datasets. Often,
these datasets are accompanied by metadata describing the who, when, where, how, and why
of data collection. Transforming metadata into KGs increases the FAIRness [2] of the data by
enhancing its reusability.
Embeddings are a well-established technique that captures the semantics of a given word
or sentence. Previous works have shown their significant impact on many Natural Language
Processing (NLP) applications [3]. In this work, we transform raw metadata files into a KG
using an embedding-based matching technique. We tested our technique on a biodiversity use
case; however, we expect our method to be domain-independent.
2. Methodology
Figure 1 shows the four phases of our pipeline. 1) Data Acquisition We collected our metadata
files from various biodiversity data portals to develop the data model and evaluate our matching
technique. 2) Ontology Development The data-driven process of crafting our data model
1
https://github.com/fusion-jena/Meta2KG
Ontology Matching @ISWC 2022
Envelope-Open nora.abdelmageed@uni-jena.de (N. Abdelmageed); birgitta.koenig-ries@uni-jena.de (B. König-Ries)
Orcid 0000-0002-1405-6860 (N. Abdelmageed); 0000-0002-2382-9722 (B. König-Ries)
© 2022 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)
Acquisition
Data
Ontology Development
Pre- 1
Reconcile & 2 Keys +
Processing Keys Model Synonyms
Get Onto 3 Embedding
BMO Embedding Source
Seen Data BMO E
Pre- Ground
Match & Populate
4
Keys Match 6
Matches Evaluate 7
Processing Truth
Embedding Get 5
Validate &
Embedding Populate
8 Scores
Source
Unseen Data Keys E
Release
Publish 9
BMKG
Figure 1: Abstract overview of our workflow to transform raw metadata to KG.
(Biodiversity Metadata Ontology (BMO)). We applied several cleaning steps to the collected data.
During this phase, we held several meetings with a biodiversity expert to validate and review
our conceptual model. In addition, we developed mean-based techniques to transform BMO
to the embedding space (BMOE). 3) Match & Populate Our unsupervised learning methods
for ontology matching and instance population. For matching, we used cosine similarity in
the embedding space between the ontological embeddings, BMO E, and metadata embeddings,
Keys E. We used embeddings to capture the semantic meaning of words. For population, We
limit the population to a triple if and only if its value has the expected datatype. For example,
we accept the triple, e.g., (author, phone, XXX) if “XXX” is a phone. We implemented such
kind of validations using regular expressions. 4) Release We published our resources and code
under the Creative Commons Attribution 4.0 International (CC BY 4.0) and Apache License 2.0,
respectively.
Acknowledgments
The authors thank the Carl Zeiss Foundation for the financial support of the project “A Virtual
Werkstatt for Digitization in the Sciences (K3, P5)” within the scope of the program line “Break-
throughs: Exploring Intelligent Systems for Digitization” - explore the basics, use applications”.
In addition, we thank, Cornelia Fürstenau, Sirko Schindler, Muhammad Abbady, and Jan Martin
Keil for the fruitful discussions.
References
[1] A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. de Melo, C. Gutiérrez, S. Kirrane,
J. E. L. Gayo, R. Navigli, S. Neumaier, A. N. Ngomo, A. Polleres, S. M. Rashid, A. Rula,
L. Schmelzeisen, J. Sequeda, S. Staab, A. Zimmermann, Knowledge Graphs, Synthesis
Lectures on Data, Semantics, and Knowledge, Morgan & Claypool Publishers, 2021. doi:10.
2200/S01125ED1V01Y202109DSK022 .
[2] M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak,
N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The fair guid-
ing principles for scientific data management and stewardship, Scientific data 3 (2016).
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[3] P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching word vectors with subword
information, Trans. Assoc. Comput. Linguistics 5 (2017) 135–146. URL: https://doi.org/10.
1162/tacl_a_00051. doi:10.1162/tacl\_a\_00051 .