=Paper=
{{Paper
|id=Vol-2849/paper-30
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2849/paper-30.pdf
|volume=Vol-2849
|dblpUrl=https://dblp.org/rec/conf/swat4ls/SharafeldeenAK19
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2849/paper-30.pdf</pdf>
<pre>
                         Species Association Knowledge Graph
                            Construction - A Demo Paper

              Dina Sharafeldeen 0000−0001−5801−4948 , Alsayed Algergawy 0000−0002−8550−4720 , and
                                  Birgitta König-Ries 0000−0002−2382−9722

                         Heinz Nixdorf Chair for Distributed Information Systems
                              Friedrich Schiller University of Jena, Germany
              {dina.sharafeldeen, alsayed.algergawy, birgitta.koenig-ries}@uni-jena.de



                      Abstract. Constructing knowledge graphs for new domains and linking
                      them to existing ones has recently gained significant attention, especially
                      in domains that have experienced a tremendous increase in available data
                      such as biodiversity research. In this demo, we show a semantic data
                      mining framework combining several knowledge bases to help in this
                      task and show the feasibility of our framework using real-world datasets
                      from a large-scale biodiversity project.
                      Keywords: Knowledge Graph, Association rules, Data mining


                 Biodiversity is a multidisciplinary, challenging research area that has expe-
             rienced a tremendous increase in the number of datasets [3]. Therefore, it is
             quite challenging, on the one hand, to extract valuable hidden knowledge from
             these complex datasets and on the other hand, to make these datasets linkable
             to other sources of knowledge to gain new insights. Knowledge graphs can be
             processed in various ways, leading to applications such as semantic search, ques-
             tion answering, and entity resolution [1]. Using robust techniques for knowledge
             graph construction automation is crucial and useful in different domains. Moti-
             vated by [5], Page proposed a biodiversity knowledge graph [2]. It is combining
             and interlinking information about biodiversity entities, such as taxa, taxonomic
             names, publications, people, species, sequences, images, and collections. Many
             questions in biodiversity can be framed as paths in this graph. We proposed a
             semantic data mining framework [4] for knowledge graph construction to extract
             hidden knowledge from species datasets combined with other knowledge sources
             like Encyclopedia of Life (EOL)1 and Global Biotic Interactions (GloBI)2 . Com-
             bining these sources results in a knowledge graph that will be accumulatively
             constructed and refined.
                 In our demo, visitors will be able to construct an association knowledge graph
             from species abundance datasets. After they load the species abundance dataset,
             they can select the attributes that specify the date of observation. Then, they
             can select the radius of the species plot, which is the area where the species
             is observed. Afterwards, we perform entities(species) disambiguation by linking
              1
                  https://eol.org/
              2
                  www.globalbioticinteractions.org




Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
       Fig. 1: Part of the knowledge graph after enrichment with GLOBI


to EOL. Then, the data is transformed into transactions to fit the association
rules extraction algorithm. Each transaction contains all the species that exist
in the same plot and on the same date. Then, the association rules mining algo-
rithm is applied to extract the species association rules. In addition, these rules
are represented in RDF format. Then, the initial association knowledge graph
is constructed as shown in Fig.1 (the entities, properties, and the relations in
the black color). Furthermore, we enrich the constructed graph by linking to
EOL getting more information like images and other accepted scientific names.
Moreover, linking to GloBI enriches the constructed knowledge graph. This en-
richment is achieved by promoting some of the extracted species co-occurrence
to concrete interactions. In another way, more enrichment is done by adding new
entities with their relationships from the GLOBI knowledge base, as shown in
Fig.1. As future work, we are working to demonstrate the general applicability
of our tool with more datasets in the biodiversity domain and other domains.

References
1. Kejriwal, M.: What is a knowledge graph? In: Domain-Specific Knowledge Graph
   Construction (2019)
2. Page, R.: Towards a biodiversity knowledge graph. Research Ideas and Outcomes
   2, e8767 (2016)
3. Shah, A.: Why is biodiversity important? who cares?—global issues. Global Issues:
   Social, Political, Economic and Environmental Issues That Affect Us All—Global
   Issues 18 (2009)
4. Sharafeldeen, D., Algergawy, A., König-Ries, B.: Towards knowledge graph con-
   struction using semantic data mining. In: The 21st International Conference on
   Information Integration and Web-based Applications & Services (In press) (2019)
5. Szekely, P., Knoblock, C.A., Slepicka, J., Philpot, A., Singh, A., Yin, C., Kapoor,
   D., Natarajan, P., Marcu, D., Knight, K., et al.: Building and using a knowledge
   graph to combat human trafficking. In: International Semantic Web Conference. pp.
   205–221. Springer (2015)



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