Adding Biodiversity Datasets from Argentinian Patagonia to the Web of Data

Abstract

In this work we present a framework to publish biodiversity data from Argentinian Patagonia as Linked Open Data (LOD). These datasets contains information of biological species (mammals, plants, parasites, among others) have been collected by researchers from the Centro Nacional Patagonico (CENPAT), and have initially been made available as Darwin Core Archive (DwC-A) les. We introduce and detail a transformation process and explain how to access and exploit them, promoting integration with other repositories.

Keywords

• Biocollections
• Darwin Core
• Linked data
• RDF
• SPARQL

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Animal, plant and marine biodiversity comprise the \natural capital" that keeps our ecosystems functional and economies productive. However, since the world is experiencing a dramatic loss of biodiversity [ 1,2 ], an analysis about its impact is being done by digitising and publishing biological collections [ 3 ]. To this end, the biodiversity community has standardised shared common vocabularies such as Darwin Core (DwC) [ 4 ] together with platforms as the Integrated Publishing Toolkit (IPT) [ 5 ] aiming at publishing and sharing biodiversity data. As a consequence, the biodiversity community now have hundreds of millions of records published in common formats and aggregated into centralised portals. Nevertheless, new challenges emerged from this initiative for e ectively using such a large volume of data. In particular, as the number of species, geographic regions, and institutions continue growing, answering questions about the complex interrelationships among these data become increasingly di cult. The Semantic Web (SW) [ 6 ] provides possible solutions to these problems by enabling the Web of Linked Data (LD) [ 7 ], where data objects are uniquely identi ed and the relationships among them are explicitly de ned. LD is a powerful and compelling approach for spreading and consuming scienti c data. It involves publishing, sharing and connecting data on the Web, and o ers a new way of data integration and interoperability. The driving force to implement LD spaces is the RDF technology. Moreover, there is an increasing recognition of the advantages of LD technologies in the life sciences [ 8,9 ].

In this same direction, CENPAT1 has started to publicly share its data under Open Data licence.2 Data are available as Darwin Core Archive (DwC-A) [ 10 ], which are a set of les for describing the structure and relationships of the raw data along with metadata les conforming the DwC standard. Nevertheless, the well-known IPT platform focuses on publishing content in unstructured or semi-structured formats but reducing the possibilities to interoperate with other datasets and make them accessible for machines. To enhance this approach, we present a transformation process to publish these data as RDF datasets. This process uses OpenRe ne [ 11 ] for generating RDF triples from semi-structured data and de ne URIs. It also uses GraphDB [ 12 ], previously known as OWLIM [ 12 ], for storing, browsing, accessing and linking data with external RDF datasets. Along this process, we follow the stages de ned in the LOD Life-Cycle proposed in [ 13 ]. We claim that this work is an opportunity to exploit data from biodiversity in Argentina because they had been never published as LOD.

This work is structured as follows. Section 2 describes the main features of the datasets selected and their relationships with DwC. Section 3 describes the transformation process to RDF, while section 4 presents its publication and its access. Section 5 shows the framework to discover links to other datasets. Next, section 6 presents the exploitation of the dataset. Finally, we draw conclusions and suggest some future improvements. 2

CENPAT Data Sources In this section, before describing our datasets, we brie y explain the DwC standard and DwC-A, which these datasets are based on. 2.1

Darwin Core Terms and Darwin Core Archive DwC [ 4 ] is a body of standards for biodiversity informatics. It provides stable terms and vocabularies for sharing biodiversity data. DwC is maintained by TDWG3 (Biodiversity Information Standards, formerly The International Working Group on Taxonomic Databases). Its terms are organised into nine categories (often referred to as classes ), six of which cover broad aspects of the biodiversity domain. Occurrence refers to existence of an organism at both particular place and time. Location is the place where the organism were observed (normally a geographical region or place). Event is the relationship between Occurrence and Location and register protocols and methods, dates, time and eld notes. 1 http://www.cenpat-conicet.gob.ar/ 2 https://creativecommons.org/licenses/by/4.0/legalcode 3 http://www.tdwg.org/ Finally, Taxon refers to scienti c names, vernacular names, etc. of the organism observed. The remaining categories cover relationships to other resources, measurements, and generic information about records. DwC also makes use of Dublin Core terms [ 14 ], for example: type, modi ed, language, rights, rightsHolder, accessRights, bibliographicCitation, references.

In the same direction, Darwin Core Archive (DwC-A) [ 10 ] is a biodiversity informatics data standard that makes use of the DwC terms to produce a single, self-contained dataset and thus sharing both species-level (taxonomic) and species-occurrence data. Moreover, each DwC-A includes these les. Firstly, the core data file (mandatory) consists of a standard set of DwC terms together with the raw data. This le is formatted as elded text, where data records are expressed as rows of text, and data elements (columns) are separated with a standard delimiter such as a tab or comma. Its rst row speci es the headers for each column. Secondly, the descriptor metafile de nes how the core data le is organised and maps each data column to a corresponding DwC term. Lastly, the resource metadata provides information about the dataset itself such as its description (abstract), agents responsible for authorship, publication and documentation, bibliographic and citation information, collection method, among others. 2.2

Dataset Features The datasets analysed belong to CENPAT and are available as DwC-A in an IPT server from this institution. They include collections of marine, terrestrial, parasites and plant species mainly registered from several points of the Argentinian Patagonia. Data are generated in di erent ways: some of them by means of electronic devices placed in di erent animals to study environmental variables, while others are observations of species in their natural habitat or species studied in laboratories. To ensure the quality of these data, the records have been structured according to the procedure described in [ 15 ].

Up to May 2017, CENPAT owns 33 datasets representing about 273.419 occurrence records, where 80% of them have been also georeferenced. Some of these collections contain unique data never published because of the age of the records (1970s). As a consequence, making this information available as LOD is so important for researchers, who are studying species conservation and the impact of man in biodiversity along the last years [ 16,17 ]. 3

Linked Data Creation Publishing data as LD involves data cleaning, mapping and conversion processes from DwC-A to RDF triples. The architecture of such a process is shown in Fig. 1 and has been structured as described in the following subsections. The DwC-A are manually extracted from the IPT repository and their occurrences les (occurrence.txt) are processed using OpenRe ne tool [ 11 ]. There, occurrences are cleaned and converted to standardised data types such as dates, numerical values, etc. and empty columns are removed. OpenRe ne also allows adding reconciliation services based on SPARQL endpoints, which return candidate resources from external datasets to be matched to elds in the local datasets. In our process, we use DBpedia [ 18 ] endpoint4 to reconcile the Country column with the dbo:country resource in DBpedia, the link between the resources is made through the property owl:sameAs. After that, if the reconciliation is done, we create a new column for the corresponding URI of the resource. In particular, we add the column named dbpediaCountryURI for the original Country.

Another reconciliation service5 used, it was based on a taxonomic database Encyclopedia of Life (EOL)6 which allows to reconcile accepted names in EOL database. Speci cally, the reconciliation is applied to the column scientificName so that we create a new column named EOL page for the EOL page describing the specie. Unfortunately, this whole process is time-consuming because not all values are automatically matched and thus ambiguous suggestions must be xed. Moreover, in this phase only two columns have been possible to reconcile because the process returns unsuitable results using DBpedia services some columns like institutionCode or locality. 4 https://dbpedia.org/sparql 5 http://iphylo.org/~rpage/phyloinformatics/services/reconciliation_eol.

php 6 http://www.eol.org/ 3.2

RDF Schema Alignment and URI De nition After cleaning and reconciling, data are converted to RDF triples using RDF Re ne7, which is an extension of OpenRe ne tool. RDF Re ne allows users to go through a graphical interface describing the RDF scheme alignment skeleton to be shared among di erent datasets. The RDF skeleton speci es the subject, predicate and the object of the triples to be generated. The next step in the process is to set up pre xes. Since datasets include localities, locations and research institutes, we set up pre xes for well-known vocabularies such as the W3C Basic Geo ontology [ 19 ], Geonames [ 20 ], DBpedia, FOAF [ 21 ], Darwin-SW [ 22 ] for establishing relationships among DwC classes and Taxon Concept.8 Table 1 shows the pre xes used.

In order to generate URI for each resource, in this approach we used GREL (General Re ne Expression Language) also provided by OpenRe ne, the general structure of the URIs is described below:

http://[base uri]/[DwC class]/[value] where: [base uri] is the one speci es in Table 1, [DwC class] is the respective DwC class and [value] is the value of the cells in the le of occurrences. It is also important to note that the generated URIs are instances of the classes de ned in the DwC standard. Finally, the resulting RDF triple for an occurrence is: SUBJECT : < base_uri / occurrence / f6bbf85d -85 ea -4605 -87 fa - d81aca73a1cd > PREDICATE : rdf : type OBJECT : dwc : Occurrence 7 http://refine.deri.ie/ 8 http://lod.taxonconcept.org/ontology/txn.owl f :n :v c -o

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Publishing and Accessing Data The transformed biodiversity data have been published, and can to be accessed, through GraphDB. GraphDB is a highly e cient and robust graph database with RDF and SPARQL support. It allows users to explore the hierarchy of RDF classes (Class hierarchy), where each class can be browsed to explore its instances. Similarly, relationships among these classes also can be explored giving an overview about how many links exist between instances of the two classes (Class relationship). Each link is a RDF statement where its subject and object are class instances and its predicate is the link itself. Lastly, users also can explore resources providing URIs representing any of the subject, predicate or object of a triple (View resource).

Finally, Fig. 2 shows the resulting graph for the description of a southern elephant seal skull, which is part of the CENPAT collection of marine mammals and contains information about where has been found, who has been collected for, sex and scienti c name, among others. Another way to access the same information is to explore the View resource in the GraphDB repository http://crowd.fi.uncoma.edu.ar:3333/resource/find for the speci c occurrence f6bbf85d-85ea-4605-87fa-d81aca73a1cd, while the serialization of the complete graph in Turtle syntax can be consulted in.9 9 https://github.com/cenpat-gilia/CENPAT-GILIA-LOD/blob/master/rdf/graph.

ttl, accessed at September 2017 Interlinking other datasets in a semi-automated way is crucial aiming at facilitating data integration. In this context, OpenRe ne reconciliation service is able to match some links to DBpedia, but since it is still limited, our process should use more powerful tools to discover links to other datasets. For this task, our approach preliminarily integrate SILK framework10 that uses Silk-Link Specification Language (Silk-LSL) to express heuristics for deciding whether a semantic relationship exists between two entities. For interlinking species between DBpedia and our dataset, we used Levenshtein distance a comparison operator that evaluates two inputs and computes the similarity based on a user-de ned distance measure and a user-de ned threshold. This comparator receives as input two strings dbp:binomial (Binomial nomenclature in DBpedia) and the combination of dwc:genus + dwc:specificEpithet (the concatenation of these two de nes the scienti c name of the species). The Levenshtein distance comparator was set up with <Thresholds = "0.0" and Weight = "1">. After the execution, SILK discovered 15 links to DBpedia with an accuracy of 100% and 85 link with an accuracy between 65% and 75%. In this case, we permit only one outgoing owl:sameAs link from each resource. The complete Silk-LSL script can be downloaded from.11

However, although a set of links has been successfully generated, users' feedback is needed to lter some species wrongly matched by the tool. Finally, we must identify further candidates for interlinking and tests other properties or classes from our dataset in order to increase the automatic capabilities of the framework. 6

Exploitation This section shows how the di erent types of observations of species can be retrieved, complemented with information of another datasets and ltered by submitting SPARQL queries to GraphDB endpoint. Moreover, it provides some experiments in R by using the SPARQL12 package. Each SPARQL query in following examples assumes the pre x de ned in Table 1.

Total Number of Species in the CENPAT Dataset. The following query retrieves the species of the dataset. To this end, it includes the scienti c name of the species and also its amount of occurrences, to execute this query in GraphDB see.13 The Fig. 3 shows only the rst resulting records. 10 http://silkframework.org/ 11 https://github.com/cenpat-gilia/CENPAT-GILIA-LOD/blob/master/SILK/ link-spec.xml, accessed at September 2017 12 https://cran.rproject.org/web/packages/SPARQL/SPARQL.pdf 13 http://crowd.fi.uncoma.edu.ar:3333/sparql?savedQueryName=species-count SELECT ? scname ( COUNT (? s) AS ? observations ) {? s a dwc : Occurrence . ?s dsw : toTaxon ? taxon .

? taxon dwc : scientificName ? scname } GROUP BY ? scname ORDER BY DESC ( COUNT (? s ))

Occurrences by Year. The following query allows to observe the temporality of the occurrences and its results are visualised using R as shown the Fig. 4. The R script is available in.14 SELECT ? year ( COUNT (? s) as ? count ) {? s a dwc : Event .

?s dwc : verbatimEventDate ? date } GROUP BY ( year (? date ) AS ? year ) ORDER BY ASC (? year )

Conservation Status of Species. Conservation status are de ned by The IUCN Global Species Programme15 and are taken as a global reference. Information about the state of conservation is missing in CENPAT datasets so that 14 https://github.com/cenpat-gilia/CENPAT-GILIA-LOD/blob/master/ r-scripts/occurrences-by-year.R, accessed at September 2017 15 http://www.iucnredlist.org/ providing these data linking other RDF datasets is highly desirable. To this end, the following query capture these missing data using the owl:sameAs property. The results are shown in Fig. 5, to execute this query in GraphDB, see.16 SELECT ? scname ? eol_page ? c_status WHERE { ?s a dwc : Taxon .

?s dwc : scientificName ? scname . ?s txn : hasEOLPage ? eol_page .

?s owl : sameAs ? resource .

SERVICE < http :// dbpedia . org / sparql > {

? resource dbo : conservationStatus ? c_status .} } picted in Fig. 6 using R, and the script is available in.17 7 Conclusions and Further Works In this work we have presented a framework to publish biodiversity data from Argentinian Patagonia as LOD, which have initially been made available as 16 http://crowd.fi.uncoma.edu.ar:3333/sparql?savedQueryName=

conservation-status 17 https://github.com/cenpat-gilia/CENPAT-GILIA-LOD/blob/master/ r-scripts/positions-ml.R, accessed at September 2017 Darwin Core Archive les. The aim is to facilitate the access of researchers to important data and thus giving a valuable support to the scienti c analysis of the biodiversity. In addition, this work is the rst Argentinian initiative to convert biodiversity data according to the criteria established by LOD.

We have detailed the transformation process and explained how to access and exploit them, promoting integration with other repositories. Moreover, we have depicted this process using queries extracted from the domain of application. Such RDF repository is hosted at http://crowd.fi.uncoma.edu.ar:3333/ together with an SPARQL endpoint, in this initial stage we store 202.119 triples.

As future works, we plan to automate some tasks of the process and interlink with more datasets. Moreover, providing easier SPARQL access for non-skilled users. Finally, we are analysing other ontologies such as ENVO [ 23 ], NCBI [ 24 ] and OWL Time [ 25 ] and working on a suite of complementary ontologies for describing every aspect of semantic biodiversity.

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