While the amount of data on the Web grows at 57 % per year, the Web of Science maintains a considerable amount of inertia, as yearly growth varies between 1:6 % and 14 %. On the other hand, the Web of Science consists of high quality information created and reviewed by the international community of researchers. While it is a complicated process to switch from traditional publishing methods to methods, which enable data publishing in machine-readable formats, the situation can be improved by at least exposing metadata about the scienti c publications in machine-readable format. In this paper we aim at metadata, hidden inside universities' internal databases, reports and other hard to discover sources. We extend the VIVO ontology and create the VIVO+ ontology. We de ne and describe a framework for automatic conversion of university data to RDF. We showcase the VIVO+ ontology and the framework using the example of the ITMO university.
While the amount of data on the Web grows at 57 % per
year [
On the other hand, the Web of Science consists of high
quality information created and reviewed by the international
community of researchers. Moreover, by publishing research
contributions using traditional methods, which are targeted
at print and screen media (i.e. PDF documents), the data
has become inaccessible for automatic processing. In the
new era of Big Data and the Web of Data, the scienti c
community started developing new publication methods, which
re ect the 3V Concept (i.e. volume, velocity, variety), such
as nanopublications [
The Data Web of Science will enable new ways of data access based on the data semantics. In particular, complex search queries based on metadata will be available. Therefore, the availability and discovery for research papers will be increased. Given the availability of annotated PDF documents it will be possible to measure key performance indicators across universities as well as browse university data world wide. Also, the Data Web of Science will facilitate search for specialists and researchers given a particular research eld, thus enabling new collaborations.
In this paper we present a framework to expose metadata
about the research institutions according to the Linked Data
principles [
We de ne and describe a framework for automatic conversion of university data to RDF.
We made the implementation of our approach freely available (on GitHub).
We showcase how academical RDF data can be utilized and integrated with other data sources in an e cient way using the example of the ITMO university in St. Petersburg.
Using an established ontology for a particular domain reduces the development e ort and leads to higher quality and better integration. In our usage scenario, we aim to publish organizational university data within a joint project of the University of Leipzig and ITMO. According to our research the best- tting candidate ontology for such a task is VIVO [3]. However, it contains a lot of gaps left which we address with the VIVO+ ontology built upon VIVO and described in this section. VIVO+ adds missing key concepts, models and relationships. Moreover, it provides a more exible and general modelling of relationships regarding academic degrees, which provides a better coverage, e.g. by allowing to represent Russian particularities.
VIVO lacks many key concepts while others are modelled in insu cient detail. For example, it contains a concept for a PhD student but this concept does not reference any other concept of this domain except its super class. While a human user can understand the concept using its label, the more ne grained modelling of VIVO+ with related concepts and their connections allows easier extension, higher expressiveness, better querying and automatic processing.
The following example shows VIVO+ modelling of the student-degree-quali cation domain. : A l i c e a f o a f : Person ; : a c a d e m i c Q u a l i f i c a t i o n : A l i c e Q u a l i f i c a t i o n ; : a s p i r e s D e g r e e : PhDDegree . : A l i c e Q u a l i f i c a t i o n : a ca d e m i cD e g r e e : MasterDegree ; : a c a d e m i c S u b j e c t : Chemistry .
Besides the core ontologies RDF, RDFS, OWL, DCMI Metadata Terms, XSD and FOAF, VIVO+ uses the LinkedGeoData and GeoNames ontologies to specify locations.
In order to categorize the research topic of a laboratory, we use the 6 digit subject matter identi ers of the Russian State Index of Scienti c and Technical Information (SISTI)1.
VIVO+ was designed with the following design principles
in mind, following [
We aim to provide an ontology that is su ciently generic to be usable internationally. Di erent countries have di erent educational systems, however and VIVO is modelled according to the system of the USA. Adapting VIVO to ITMO University poses three challenges: (1) identifying the peculiarities of the Russian system (2) modelling the resulting additional concepts and (3) appropriately linking them to existing concepts. One di erence between Russia and most of the world is that it has two di erent doctoral degrees: the Candidate of Sciences (кандидат наук, kandidat nauk) and the Doctor of Sciences (доктор наук, doktor nauk). The Candidate of Sciences is equivalent to the PhD while the Doctor of Sciences can be earned after a period of further study following the award of the Candidate of Sciences degree and requires ve to fteen years beyond the award of the Candidate of Sciences. VIVO+ contains classes for both degrees and relates them to existing concepts. 3.
In this section, we describe the transformation of the university data from RDBMS to RDF modeled according to the VIVO+ ontology. To perform such a transformation, we developed kOre2 framework on top of the Sparqlify SPARQLSQL rewriter3, depicted in Figure 1. The framework is evaluated using ITMO university infrastructure (i.e. Oracle RDBMS) in section 4.
The main components of the kOre framework are: The Mappings Repository contains mappings in SML (Sparqlify Mapping Language). Mappings are used by the Sparqlify SPAQRL-SQL rewriter to convert the data from RDBMS to RDF. Mappings have to be maintained and updated by the framework maintainer in order to re ect university RDBMS schema changes and tackle new data inside RDBMS.
The Sparqlify SPARQL-SQL rewriter establishes the connection to RDBMS and converts the data to RDF using mappings from the Mappings Repository.
The Triple store stores RDF data and executes queries over it.
The SPARQL Endpoint publishes RDF data on the Web thus enabling various web applications which are capable of using the W3C semantic web standards.
We de ne interactions between components with four basic operations: 1. Poll. As a university RDBMS is a live system updated with new information several times a day, Sparqlify SPARQL-SQL rewriter polls the Mappings Repository periodically to check for changes and perform transformations. 2. Push. After successful transformation of the data from the RDBMS the data has to be pushed to the Triple store. The Push operation adds/deletes/updates RDF data inside the Triple store. 2kOre: Using Linked Data for OpenScience Information Integration. 3http://aksw.org/Projects/Sparqlify.html poll push publish
transform
PREFIX ifmolod:<http://lod.ifmo.ru/> PREFIX vivoplus:<http://vivoplus.aksw.org/ontology#> PREFIX vivo:<http://vivoweb.org/ontology/core#> PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#> PREFIX xsd:<http://www.w3.org/2001/XMLSchema#> CREATE VIEW LaboratoryResearchFields AS CONSTRUCT { ?laboratory vivoplus:locatedIn ?country . } WITH ?laboratory = uri(concat("http://lod.ifmo.ru/Laboratory", ?NET_DEP_ID)) ?country = uri(?URI) FROM [[ SELECT
NET_DEP_ID, URI FROM sem_country_info, sem_country_lgd WHERE country_id=id
Mapping Example consume
Mappings
Repository
3. Publish. The Triple store publishes the data by providing a SPARQL Endpoint thereby making it accessible on the Web. 4. Consume. Web applications can consume RDF via the SPARQL Endpoint, which provides a set of interfaces (e.g. RDF/JSON interface).
The presented framework provides a persistent layer on top of RDBMS infrastructure. Schema changes of underlying RDBMS are re ected with SML mappings, therefore publicly available data provided by the framework is consistent over time. Thus data consumers can rely on data schema provided by the kOre framework and save development time required for tackling schema changes inside applications.
We implement and deploy the kOre framework using infrastructure of ITMO (Saint Petersburg State University of Information Technologies, Mechanics and Optics) University.4 ITMO university uses an Oracle RDBMS to store the data about laboratories, people, publications among others. The database is only accessible from the university network, therefore a VPN connection is necessary.
For deployment we set up an Ubuntu 14.04 server with 6 GB of RAM and 35 GB of disk space. The Mappings Repository on the deployed framework corresponds to a system folder. Sparqlify is installed for all users and available through command-line interface. As a Triple store we use Virtuoso Open Source, which provides SPARQL Endpoint with RDF/JSON interface out of the box. Poll and push operations are con gured using shell scripts and scheduled with crontab. Publish operation is performed by Virtuoso Open Source internally. Consume operation is possible through the SPARQL Endpoint using POST requests.5 4The implementation is freely available on GitHub: https: //github.com/AKSW/itmolod 5The SPARQL endpoint for ITMO LOD project is located at http://lod.ifmo.ru/sparql URI Version Date Version Number License Triples SPARQL Endpoint N-Triples Dump http: //vivoplus.aksw.org/ontology# 2014-11-01 1.0 PDDL 1.07 15 572 http://lod.ifmo.ru/sparql http: //lod.ifmo.ru/data.itmo.nt
At the moment of writing, the ITMO LOD dataset contains information about 43 laboratories, 188 research areas and 1001 persons. The dump of the dataset is available online8. Table 1 provides statistics and links to services such as the SPARQL endpoint. The dataset is openly licensed under the PDDL 1.0. in accordance with the open de nition9. 5.
In this section we describe how the consume operation can be utilized by developers and end-users.
For end-users we published the data from ITMO LOD SPARQL endpoint under lod.ifmo.ru domain using the Pubby10 Linked Data interface. Pubby makes dataset URIs dereferenceable and enables navigation between linked entities inside the dataset with the easy-to-use web interface. For example, in Figure 2 we show, that a user is able to navigate through persons and research areas for a particular laboratory.
Developers can consume the data through SPARQL endpoint. Here we showcase how the ITMO LOD dataset can be utilized in such a way by implementing LabMap application11 (the code is published in the GitHub repository), which shows 8http://lod.ifmo.ru/data/itmo.nt 9http://opendefinition.org/ 10http://wifo5-03.informatik.uni-mannheim.de/pubby/ 11http://lod.ifmo.ru/usecases/heatmap.html http://lod.ifmo.ru/page/Laboratory87847 Laboratory View vivoplus:locatedIn vivoplus:hasResearchArea ...
http://lod.ifmo.ru/page/Person100628 Person View vivo:affiliatedOrganization foaf:firstName foaf:lastName links http://lod.ifmo.ru/page/ResearchArea..1. 841 Research Area View vivoplus:hasResearchArea foaf:firstName foaf:lastName ... links Laboratory View laboratories of ITMO university by collaborating countries. User is able to see the list of laboratories per country as well as persons working in particular laboratory (see Figure 2). [3] VIVO-ISF Ontology. https://wiki.duraspace.org/ display/VIVO/VIVO-ISF+Ontology, accessed: 11-062015
We implemented the the kOre framework, which facilitates data publishing for universities. To support our framework we extended the VIVO ontology resulting in VIVO+. To showcase the applicability of the kOre framework we deployed Linked Data interface for end-users implemented simple web application as an example of data consumption for developers.
Unlocking the hidden metadata for universities is a step forward in the integration e ort between the original Web of Science and the Data Web of Science. In the future we plan to deploy the kOre framework for University of Leipzig. We plan to support ongoing e ort of ITMO university to expose more data publicly. Also, we plan to involve scienti c personnel and students into the development of interesting applications using available data.
We want to say thank you to our colleagues in AKSW and ITMO university, whom supported ITMO LOD project: Claus Stadler for adapting Sparqlify to Oracle SQL on our request.
Maxim Kolchin for administrating the server environment.
Denis Varenikov for helping getting access to ITMO RDBMS and exposing data for us.
This work was partially nancially supported by Government of Russian Federation, Grant 074-U01.