=Paper=
{{Paper
|id=Vol-1117/paper9
|storemode=property
|title=Simple visualization of structures of interrelated concepts in the FRBRoo ontology
|pdfUrl=https://ceur-ws.org/Vol-1117/paper9.pdf
|volume=Vol-1117
|dblpUrl=https://dblp.org/rec/conf/ercimdl/SielskiW13
}}
==Simple visualization of structures of interrelated concepts in the FRBRoo ontology==
https://ceur-ws.org/Vol-1117/paper9.pdf
Simple visualization of structures of interrelated concepts
in the FRBRoo ontology
Krzysztof Sielski, Marcin Werla
Poznań Supercomputing and Networking Center,
ul. Noskowskiego 12/14, 61-704 Poznań, Poland
{sielski,mwerla}@man.poznan.pl
Keywords: Semantic Web, ontology, OWL, RDF, FRBRoo, RDF Unit,
knowledge base
The Knowledge Base which was created by Poznań Supercomputing and Networking
Center (PSNC) as a part the SYNAT1 project integrates information from distributed
heterogeneous sources such as digital libraries, digital museums, scientific and
technical information systems. The gathered knowledge is stored in an RDF semantic
database and is represented in FRBRoo ontology with some custom extensions, which
had to be introduced in order to represent all the information without any semantic
loss.
As of the beginning of 2013, the Knowledge Base contained information from over
3,100,000 metadata records, which were originally encoded in various schemas:
PLMET (data obtained from Polish Digital Libraries Federation), MARC 21 XML
(from union catalog of Polish research libraries NUKAT), MONA (from the National
Museum in Warsaw) or CDWA LITE (from the National Museum in Krakow). These
records were converted to FRBRoo ontology using jMet2Ont[1] tool. Some auxiliary
data sources such as VIAF, Geonames, KABA Subject Headings and Lexvo have
been used to enrich the records with detailed information. Currently, the number of
RDF triples building the Knowledge Base is 536M, which includes 235M explicit and
301M implicit triples. The implicit triples have been added by the inference engine
with our custom rule set, which is a subset of OWL 2 RL/RDF entailment rules.
Unlike traditional relational databases, data represented as triples does not have a
precise schema with strict constraints. Instead, OWL ontologies describe the structure
of concepts and relations between them. As FRBRoo is a complex ontology with
many classes, this model is often converted to a simpler one when presented to an
user. The contents of the Knowledge Base can be explored in a couple different ways:
- a raw SPARQL endpoint, which is aimed at expert users who know the
ontology very well and have precisely defined goals;
1
SYNAT project, financed by Polish National Center for Research and Development (grant
number: SP/I/1/77065/10), is aimed to conduct a research task titled “Creation of universal,
open, repository platform for hosting and communication of networked resources of knowledge
for science, education and open society of knowledge”
- a full text search application, which searches for keywords provided by user
in RDF literals from the triplestore and uses the Query Processing Module (QPM)
which maps on-the-fly information represented in the FRBRoo ontology to a
simplified model, consisting of the following concepts: works, items, persons, places,
legal bodies, and subjects;
- a geographical search application, which allows user to select an area on a
map to find all objects connected with places contained in that area (e.g. all
publications whose subject is a particular city);
- an application to explore semantic database with dynamically fetched
portions of data describing particular object from the triplestore, which are presented
as interrelated FRBRoo concepts in a legible way understandable by non-experts.
The last named application was built as a proof of concept of RDF Unit[2]. RDF
Units are graphs which consist of several ontology objects of different classes that are
needed to provide all the essential information about a certain resource. For example,
an RDF Unit for a particular instance of Publication Expression from the Knowledge
Base would include objects representing its Title, Publication Event and Place of
Publishing, but not geographical coordinates of that place. RDF Units are dynamically
constructed based on the metaproperties of ontology relations and actual data in the
triplestore.
Such graphs are transformed into a tree structure, in which the examined resource
becomes a root. Then, the obtained RDF Unit tree is prepared for presentation by
replacing names of predicates with more user friendly labels and by flattening some
long predicate paths to a single dummy edge in order to provide information in a
straightforward way. This transformations are represented as a set of rules which take
into consideration a predicate and classes of a subject and an object. Examples of such
rules include (here [?] stands for any class):
- [E21_Person] P100_i_died_in [E69_Death] P4_has_time_span
[E52_Time-Span] P1_is_identified_by [?]→ date of death
- [F18_Serial_Work] P148_has_component
[F14_Individual_Work] → series element
- [?] P9_consists_of [F28a_Contribution] P14_carried_out_by
[?] → contributor
- [?] P9_consists_of [F28a_Contribution] P2_has_type [?] →
in the role of
Figure 1 presents a result of mapping one record in MARC 21 XML schema to
FRBRoo. It is a graph of 47 connected FRBRoo objects represented by 108 RDF
triples. Figure 2 presents a view in our application that represents an RDF Unit of an
Individual Work resource which was created in mentioned mapping. This unit
contains all the information from source record except for author's and contributors'
dates of life, which can be examined in those resources’ view.
Fig. 1 A result of mapping a single metadata record from MARC XML to FRBRoo represented
as a graph. An image in high resolution can be viewed at http://bit.ly/frbroo_ham
Fig. 2 A representation of F14_Individual_Work object (Hamlet by Shakespeare) from Fig 1
converted to a simplified tree compatible with FRBRoo ontology for presentation
The described Knowledge Base browser application was prepared for dynamic
viewing of FRBRoo data from the triplestore, but this approach is generic and should
work for another ontologies as well. It uses no predefined SPARQL queries and is
based only on a relatively small configuration: a set of graph path flattening rules for
presentation and a set of single metaproperty for each ontology predicate which is
used to build RDF Units.
References
1. Walkowska, J., Werla, M.. (2012). Advanced Automatic Mapping from Flat or Hierarchical
Metadata Schemas to a Semantic Web Ontology. TPDL 2012. Lecture Notes in Computer
Science, vol. 7489, pp. 260-272.
2. Sielski, K., Walkowska, J., Werla, M.. (2013). Methodology for Dynamic Extraction of
Highly Relevant Information Describing Particular Object from Semantic Web Knowledge
Base. TPDL 2013. Lecture Notes in Computer Science, vol. 8092, pp. 260-271.