=Paper=
{{Paper
|id=Vol-1292/ipamin2014_paper2
|storemode=property
|title=Linked Open Data System for Scientific Data Sets
|pdfUrl=https://ceur-ws.org/Vol-1292/ipamin2014_paper2.pdf
|volume=Vol-1292
|dblpUrl=https://dblp.org/rec/conf/konvens/BaumerGJGJ14
}}
==Linked Open Data System for Scientific Data Sets==
https://ceur-ws.org/Vol-1292/ipamin2014_paper2.pdf
Linked Open Data System for Scientific Data Sets
Frederik Simon Bäumer Jangwon Gim* Do-Heon Jeong
Heinz Nixdorf Institute, HNI Korea Institute of Science and Korea Institute of Science and
University of Paderborn Technology Information, KISTI Technology Information, KISTI
Paderborn, Germany Daejeon, South Korea Daejeon, South Korea
fbaeumer@hni.upb.de jangwon@kisti.re.kr heon@kisti.re.kr
Michaela Geierhos Hanmin Jung
Heinz Nixdorf Institute, HNI Korea Institute of Science and
University of Paderborn Technology Information, KISTI
Paderborn, Germany Daejeon, South Korea
geierhos@hni.upb.de jhm@kisti.re.kr
ABSTRACT 1. INTRODUCTION
In this paper, we present a system which makes scientific data
available following the linked open data principle using standards The increase in the number of datasets including scholarly
like RDF und URI as well as the popular D2R server (D2R) and publications in the Linked Data cloud shows the importance of
the customizable D2RQ mapping language. Our scientific data Linked Data for the scientific community. There are many
sets include acronym data and expansions, as well as researcher different providers for scientific data available on the Web.
data such as author name, affiliation, coauthors, and abstracts. The Publishing houses, digital libraries, and resellers have popular
system can easily be extended to other records. Regarding this, a data sources. The information types range from author
domain adaptation to patent mining seems possible. For this information (e.g. full name, affiliation, email), over publications
reason, obvious similarities and differences are presented here. (e.g. title, abstract, coauthors), to specific data like acronyms and
their related expansion. Each of these types has own requirements
The data set is collected from several different providers like on the data presentation and storage, but they all are somehow
publishing houses and digital libraries, which follow different interlinked with each other.
standards in data format and structure. Most of them are not
supporting semantic web technologies, but the legacy HTML One common way to represent this interlinks are network models.
standard. The integration of these large amounts of scientific data This database model is a generalized graph structure without any
into the Semantic Web is challenging and it needs flexible data hierarchical restriction, which allows storing objects with their
structures to access this information and interlink them. individual relationships. This is a common way to store data, but
does not fit our requirements for modern data publishing.
Based on these data sets, we will be able to derive a general
technology trend as well as the individual research domain for A more promising way to share data is the Web of Linked Data.
each researcher. The goal of our Linked Open Data System for The main idea of Linked Open Data (LOD) is to publish free
scientific data is to provide access to this data set for other accessible, structured data and to interlink it with other data. This
researchers using the Web of Linked Data. Furthermore we interlinking generates more valuable information under the
implemented an application for visualization, which allows us to consequent implementation of standard Web technologies such as
explore the relations between single data sets. RDF or URI. The core benefit for further data exploration is the
ability to apply complex graph queries, which allow the
Categories and Subject Descriptors interlinking, combining and modifying of data.
D.2.12 [Interoperability]: Data mapping For publishing scientific data stored in relational databases, a data
E.2 [Data Storage Representations]: Linked representations bridge is needed. For that reason we present our three-component
LOD system for scientific data sets, based on the popular D2R
General Terms server and the customizable D2RQ mapping language. For further
Standardization, Languages data exploration, we integrated RelFinder, an application that
visualizes relationships between RDF objects enables the
exploration of data interactively. We will demonstrate the
Keywords functionalities of our system on a test set.
Linked Open Data, Researcher Data, Acronym Data, D2R
*: Co-responding author
2. RELATED WORK
A lot of work regarding Linked Open Data and the Semantic Web
Copyright © 2014 for the individual papers by the papers' authors. is already done. Popular tools like the D2R server or standards
Copying permitted for private and academic purposes. This volume is
like RDF, HTTP and SPARQL are well proved and used in many
published and copyrighted by its editors. Published at Ceur-ws.org
Proceedings of the First International Workshop on Patent Mining and
LOD systems [1].
Its Applications (IPAMIN) 2014. Hildesheim. Oct. 7th. 2014. A major technological advantage is the backward compatibility,
At KONVENS’14, October 8–10, 2014, Hildesheim, Germany. for example, to relational databases (RDB) because the majority
of data on the current Web is stored in this kind of databases. The opens up new possibilieties in the way they can be accessed and
process of mapping RDB to RDF is subject of current research queried. For that purpose, a force-directed graph layout which
and different approaches like D2RQ, Triplify or R2RML were supports every RDF know-ledge base, that provides standardized
created [5]. R2RML currently developed by the W3C with the SPARQL access, is implemented [9]. Relationships between
main goal to define a RDB to RDF mapping standard for read- objects can be identified much easier by exploring the data step by
only data access. A different approach is Triplify. It is a very step. Features like highlighting, previewing, and filtering are
lightweight plugin for existing Web applications, which makes available for further support [4]. The class filter and the link filter
database content available as RDF and other formats. allow it to hide objects which contain specific classes or links.
RDB to RDF mapping can be done by applications such as D2R Primary these are objects, which are in this particular case not of
server. It is a java-based application for publishing the content of interest. In addition to these, the length filter and the connectivity
relational databases on the Semantic Web and for providing RDF filter allow a further selection by hiding objects with a specific
and HTML representations of resources. The D2RQ language is amount of links and relationships [9].
used for the mapping, which is popular because of the possibility RelFinder is often used as stand-alone application but also as
to provide access via SPARQL queries very easily [5]. integrated visualization application. It is for example part of the
The ability to interlink resources under the use of “typed DBpedia Viewer, an integrative interface for DBpedia, which
relationships” allows a goal-oriented navigation trough the combines LodLive as one more different visualization approaches
database content by web browsers as well as crawler applications next to RelFinder. The LodLive application is a web-based tool
[2]. Because these LOD systems and components are very that allows the exploration of Linked Data in an intuiitive and
flexible, it is possible to adapt them to different domains. interactive way [10]. In comparison, both applications provide a
Interlinked User-generated content from social networks, a Linked good visualization. RelFinder can convince with a higher browser
Open Drug Data (LODD) for pharmaceutical research and compatibility. Especially when using the Internet Explorer,
development or a LOD live database of semantically enriched LodLive throws JavaScript errors drawing several relations
sensor data, are only few successful examples, using the D2R between classes.
server [3].
Latif, Afzal and Maurer [13] utilized the D2R server to publish 3. LOD SYSTEM FOR SCIENTIFIC DATA
unstructured datasets of the Journal of Universal Computer The scientific data sets are collected from different resources and
Science (J.UCS) as Linked Data in the Web. The linked Open based on different formats. For that reasons, it is a difficult task to
Data project provides a new way to publish machine readable combine information such as researchers, affiliations or
structured data on the Web and best practices for interlinking publications and to provide them as a clean, interlinked data set
these structured datasets. Moreover, the increase in the number of under the requirements of quality. To make a contribution to the
datasets including scholarly publications in the Linked Data cloud existing Web of Linked Data, it is necessary to publish it in a
shows its importance in the scientific community. In order to take standardized, structured way under the use of common
advantage of benefits of LOD projects, the legacy HTML data in vocabularies and over a common server framework. Furthermore,
this journal is converted into machine-readable and structured it has to be identified which information can be bundled to a data
RDF data using the D2R server. It is considered to be an set containing relevant data and how this data can be usefully
appropriate for data conversion due to its good performance, interlinked. For this reason, we developed a LOD system for
scalability and the availability of SPARQL endpoint and explorer scientific data sets, which can handle all related information like
features. A RDF graph converted from the legacy HTML data has researcher data or publications and publish them in a structured
been made available in Linked Data cloud for the data reuse and way under the use of common semantic web technologies like
interlinking. Moreover, structured journal data was interlinked RDF and SPARQL. The interlinked data becomes more useful
with Linked Data resources and it successfully disambiguated and and can be easily adapted to other research topics.
interlinked datasets of authors and publications with DBpedia,
DBLP and Faceted DBLP as well as CiteULike. 3.1 System architecture
The system architecture in Figure 1 can be divided in three main
In addition, Mitrevski, Javanovik and Stonjanov [11] identified
components. The first component contains the datasets, which
some issues regarding the D2R server, which appear during the
include the specific data, as a result of different acquisition and
process of publishing the open data of the faculty of computer
preprocessing steps we applied before. Because these data sets are
science and engineering in Ss. Cyril and Methodius University.
stored in a relational database, a Linked Data view on the existing
One problem is that these relational databases include some
database is needed (data bridge). A SPARQL endpoint with the
confidential data about employees and students. However, the
ability for serving Linked Data views on relational databases is
D2R server does not provide a way to convert only specific parts D2R Server, which is part of the second step.
from the database into data in a semantic web format. Moreover,
it lacks of functionality enabling the user to link existing We chose D2R, because it is one of the most important and most
ontologies to the tables. These issues can be solved by creating a mature relevant solutions.
new database called Open Data DB including only the data with The second step, called “Linked Data System”, is responsible for
no privacy infringement as well as building the mapping tool the declarative mapping between the schemata of the database and
utilizing functionalities of the D2R server, but linking the data the target RDF terms, based on mapping rules. These rules are
with ontologies. Although a mapping tool was created, the study stored in mapping files and are formalized under the use of the
presents some improvements of this system such as automatic popular D2RQ mapping language. Each rule defines in detail how
proposal ontology annotations. resources are identified and how they have to be handled (e.g. find
For a structured representation of semantically annotated data and property values) in the SPARQL endpoint. SPARQL is a strong
a more intuitive exploration, RelFinder has been created by Heim query language for databases, which allows it to access and to
et al. The main idea is that a structured representation of RDF data modify RDF data. The Endpoint in the third step is able to
translate SPARQL based queries into SQL queries, which allows introduced our own “InSciTe” prefix and related properties. They
a live database access, even for non-SQL compatible applications. may be replaced during the further working process and should be
The “Application System” is the third step. It allows exploring the seen as temporary.
data sets visually. With this application, researchers can find new
latent interconnections in the database, like for example an
exceptionally usage of rare acronyms by an individual author. Table 1. Common vocabulary used for scientific data sets
This exploring is based on the RDF query language SPARQL Data field Property
from the second step.
Abstract pmlp:hasAbstract
Acronym InSciTe:Acronym
Affiliation sch:affiliation
Author dc:creator
Coauthor sch:contributor
Editor sch:editor
Editor Email sch:email
Element type (e.g. journal) InSciTe:ElementType
Authors Email sch:email
Figure 1. LOD system architecture for scientific data sets. Expansion InSciTe:Expansion
Source URL sch:isBasedOnUrl
3.2 Data Set Title deri:hasTitle
Digital resources like the Digital Bibliography & Library Project
Year of publication sch:datePublished
(DBLP) and publishing houses like Springer, IEEE or Elsevier
serve as data providers. Unfortunately, like already mentioned, the
data quality is inconsistent between the data providers. For that
reason, several refinement steps were applied to the data set, 3.4 Design of URIs
especially for the ambiguity detection. During the work is still in Linked data is based on URIs which identify things and enable
process, we plan to apply more algorithms for named entity users and computer-based agents to refer to these things or look
disambiguation under the goal of an increasing data set quality. them up. In this case URIs identify entities like researchers and
The researcher data set contains 8,370,074 publications like PhD show their relationships to other researchers or publications.
theses, articles or online resources. These publications come with The D2R server is managing the URIs mostly automatically. For
additional information, for example abstracts, author names, the class overview pages, we applied the following structure:
coauthors, year of publication and affiliations. Furthermore, we
identified acronyms as well as the individual expansion based on “http://{ip}:{port}/directory/{classname}s”
publication’s abstracts. This information is also part of the For individual resources like researcher we applied:
researcher data set, because they can support a future
disambiguation of researchers. One of our research subjects “http://{ip}:{port}/page/{classname}/{id}”
applied on these datasets is for example the detection of
technology trends and the identification of the research domain of 4. IMPLEMENTATION
individual researchers. Trough the data visualization, we expect to In this section, we explain the current state of the system’s
find more latent relationships between objects and classes, which implementation, including the D2R server and the RelFinder
allow us to disambiguate single named entities. visualization.
3.3 Common vocabularies 4.1 Test data set
Common vocabularies are necessary to enhance the For this project, we created a test set of 60 researchers and 400
interoperability between concepts. For that reason we use them, as related publications as well as 454 publication-related acronyms
far they already exist and exactly describe the data field. and expansions. Researchers were selected by their number of
For the interlinking of the data and the automatic processing, the publications, which has to be at least two. KISTI has diverse
exact description of an attached common vocabulary is very scientific data sets, which are derived from papers, patents and
important. For person related information we use for example the others. In order to find more valuable relationships from those
schema.org types and properties. data sets, we extracted acronyms and expansions. The number of
test data is 491,982. Using these acronyms and expansion we can
One example for a prefix is “dc”, which is commonly used for the
apply these data sets to analyze technology trend and we can find
Dublin Core Meta Initiative Terms (http://www.dublincore.org).
specific researchers who can be an expert about these acronyms or
We introduced “sch” as another prefix, which describes the
expansions [7]. The average number of expansion about an
schemas of schema.org (http://www.schema.org). For the data
acronym is 2.9. We observed that the distribution of expansions
field “Acronym”, “Element type” and “Expansion” no fitting
follows Zipf’s law. We separated each acronym name based on its
vocabularies are known at the moment. Because of that, we
semantics because an acronym name can be ambiguous. Therefore, our current work, because more standardization and comparability
each expansion name can have its own acronym name and its own is needed in order to interlink this data field. The acronyms are
URI. In order to get more valuable analysis reports by using interlinked within an own class, which contains the expansion and
acronyms and their expansions, we have to create relationships other related publication.
between these data sets and other resources such as Linked Open An example graph made by RelFinder’s visualization application
Data, SNS data, Freebase, DBPedia and others. Finally, we get is shown in Figure 4. Instead of an additional HTTP server like
more information from those relationships. Apache, we implemented the RelFinder application in the already
existing D2R web server. That way, no difficult setup is needed in
4.2 DataHub System based on LOD order to start our system – all components are loaded during
The publications view is shown in Figure 2. This view contains
program’s initiation.
information concerning the publication itself, but also further
information like coauthors, which have the property
“sch:contributor” or acronyms. There are two detected acronyms,
which are interlinked with an own class. This class contains the
expansion and another related publication, which also contains
this acronym in the same meaning. This allows us to find related
publications based on acronyms as a first indication for the
following classification.
Figure 4. Data visualization with RelFinder
Here, we added two researcher resources and one acronym. An
edge shows the relation between two RDF objects in a
unidirectional way. RelFinder looks up the relations between
these resources and draws a graph. In this example, the two
researchers have one publication in common (red relation). Or in
other words: Both researchers are creators (authors) of this
publication. Furthermore the acronym is related to the second
researcher trough another paper.
To build this relations, a n:m database table is needed, which
Figure 2. View on a specific publication (excerpt). contains one identifying ID for the publication and one for the
Furthermore, the email address of the main author is shown. It is author, which we call ‘sequence number’. The D2R server
part of the publication, because email addresses can change from interlinks the affiliation data with the publication data, based on
publication to publication. During the work is still in process, the this additional table. This table has to be extended for all
“sch:editor” data field is not finished yet, because it contains more publications and researchers as part of the further research.
than one author divided by a pipe symbol. This will be part of the
following work. Figure 3 shows for example the person view, 5. TRANSFERABILITY
which contains information about a specific researcher. Because of the open architecture, this system can easily be
adapted to other domains. In recent years, patent mining has
gained in popularity. Considerations for data acquisition and data
providing were investigated considering several aspects [14][15].
The use of RDF technology is also discussed in the area of patent
mining and implemented, for instance, as information retrieval
system for biomedical patents by Mukherjea and Bamba [14].
Furthermore, it visualizes the connections between patents, but
does not allow any user interaction.
The presented LOD system can be applied very well to the patent
mining and expand existing approaches by the factor of
information integration in the semantic web (data providing). The
objects of interest, for example, are inventors, assignees, titles,
Figure 3. View on a specific researcher
abstracts etc. This information can be interlinked by relations like
“refers”, “invented”, “assigned” etc. [14]. The semantic
In this case the researcher has one publication which is interlinked representation of patents as well as of academic documents is
by the “is dc:creater of” property. Furthermore an email address similar. Both document types can be divided into two parts: The
and the affiliation are given. The affiliation data field is subject of
document structure and the content [15]. Common vocabularies RDF knowledge bases. In Semantic Multimedia, pp. 182-
are available for both information sources. 187, Springer Berlin Heidelberg.
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6. CONCLUSIONS RDB-to-RDF mapping languages. In Proceedings of the 7th
The main idea of this paper is to unify scientific data such as International Conference on Semantic Systems, pp. 25-32,
researcher information, publications and acronyms as well as their ACM.
expansions from several original resources and to provide them as
[6] Jentzsch, A., Zhao, J., Hassanzadeh, O., Cheung, K. H.,
machine-readable and structured RDF graphs, which allow
Samwald, M. and Andersson, B. 2009. Linking open drug
interlinking and automatic processing. For this reason we
data. In I-SEMANTICS.
introduced a LOD system for scientific data sets.
[7] Kim, J., Hwang, M., Jeong, D. H. and Jung, H. 2012.
Based on the data visualization trough RelFinder, the system can Technology trends analysis and forecasting application
further help to identify latent relations between researchers based based on decision tree and statistical feature analysis. In
on publications, acronyms or for example co-authors and further Expert Systems with Applications, 39(16), pp. 12618-12625.
to disambiguate single objects and classes.
[8] Le-Phuoc, D., Parreira, J. X., Hausenblas, M., Han, Y. and
As above-mentioned, this is work in progress and we want to Hauswirth, M. 2010. Live linked open sensor database. In
apply further algorithms on the data sets to solve existing Proceedings of the 6th International Conference on Semantic
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Additionally we will expand the linked properties between single
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It could further be shown that the system is portable due to its the 15th international conference on Intelligent user
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This work was supported by the KISTI [K-14-L02-C03-S03, [12] Samwald, M., Jentzsch, A., Bouton, C., Kallesøe, C. S.,
Development of Technologies for S&T Text Big Data Analytics Willighagen, E., Hajagos, J. and Stephens, S. 2011. Linked
Application Platform] and a grant from the University of open drug data for pharmaceutical research and
Paderborn, Germany. development. In Journal of cheminformatics, 3(1), p. 19.
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