=Paper=
{{Paper
|id=Vol-1937/paper1
|storemode=property
|title=Knowledge Organisation for Digital Libraries
|pdfUrl=https://ceur-ws.org/Vol-1937/paper1.pdf
|volume=Vol-1937
|authors=Lena-Luise Stahn,Ingetraut Dahlberg,Ernesto William De Luca
|dblpUrl=https://dblp.org/rec/conf/ercimdl/StahnDL17
}}
==Knowledge Organisation for Digital Libraries==
https://ceur-ws.org/Vol-1937/paper1.pdf
Knowledge Organisation for Digital Libraries
Lena-Luise Stahn1 , Ingetraut Dahlberg, and Ernesto William De Luca1
Georg Eckert Institute Leibniz Institute for International Textbook Research
stahn@leibniz-gei.de, Ingetraut.Dahlberg@t-online.de,
deluca@leibniz-gei.de
Abstract. Today research data and output from nearly every disci-
plinary and interdisciplinary domain exist also (and sometimes only)
in digital form. Consequently, to ensure the data’s efficient retrieval as
well as long term usage and relevance, Knowledge Organisation and its
tools/systems, i.e. lexical resources like thesauri and ontologies, play a
major role in the Digital Libraries world.
In this paper we discuss our approach of pursuing the conversion and
mapping of two of the most promising lexical resources, the Informa-
tion Coding Classification and the MultiWordNet, after having converted
them into the EuroWordNet RDF/OWL format. In a second step we
show how to integrate this additional knowledge into a domain Knowl-
edge Organisation System (KOS). In the end we will have a presentation
of the method and mapping as well as a use case for its evaluation for
Information Retrieval purposes.
Keywords: Knowledge Organisation, Lexical Linked Open Data, Digi-
tal Libraries
1 Introduction
1.1 Knowledge Organisation Systems (KOS), Linked Open Data
(LOD) and Digital Libraries: Current Situation and Problems
The relevance of Knowledge Organisation has gained in importance with the
emergence of the digital era and the World Wide Web. Just as in the physical
world these large arising digital libraries require adequate and thorough struc-
turing systems in order to organise the increasing amount of available data and
to avoid loss of information. Making them accessible by ”putting them into the
Semantic Web” requires thorough description and homogenisation, i.e. the provi-
sion of a data model capable of dealing with large amounts of data from various
domains.
”A KOS serves as a bridge between the user’s information need and the
material in the collection. With it, the user should be able to identify an object
of interest without prior knowledge of its existence.”1
1
https://www.clir.org/pubs/reports/pub91/1knowledge.html
2
The Georg Eckert Institute2 (GEI) deals with various Textbook Research
resources from multiple disciplines. Research data, produced within in a specific
context under particular research questions, results a broad spectrum of digital
resources, e.g. a digital collection of historic textbooks3 , repositories for curric-
ula4 and textbook research related publications5 , as well as digital information
services6 .
Because there still does not exist a proper Knowledge Management at the
GEI as would be provided by using a suitable KOS these research results
can not yet be linked to other research contexts and therefore are in danger of
not being used afterwards. Their production within a highly specialized research
community with complex but separate contexts and systems prevents them from
being found easily, consequently followed by death of data”, double work and
waste of resources. Another researcher with related information interests has no
knowledge about the already existing GEI data and is not able to satisfy their
need of information easily.
Although many data models and classification systems have been developed
to help here, their inadequacy is evident, as these models, too, have their intrin-
sic structures and characteristics and are most often bound to their respective
domain. Digital Libraries suffer from too specialised models, which are not able
to communicate with each other. The advantages of the Linked Data World
(Berners-Lee et al. 2001) are therefore not being exploited (De Luca/Dahlberg
2014, 9). Our idea is to transport this knowledge into the Digital Libraries
world. With this a link may be enabled, e.g. between two disciplines seemingly
as slightly related as the textbook research and the restoration domain, which
eventually enriches both knowledge domains. Pursuing our preliminary work (De
Luca/Dahlberg 2014) in section 2.2, we will show our approach of improving this
situation by using broader KOS like Top Ontologies and Lexical Resources (e.g.
WordNet Domains and Information Coding Classification) and how to integrate
these into the Linked Data Cloud. In this paper, we give an overview of Ingetraut
Dahlberg’s Information Coding Classification and its potential for Information
Retrieval and Knowledge Organisation in the Digital Libraries world (see sec-
tion 2.2.1). Section 2.2.2 covers Ernesto William De Luca’s work of converting
EuroWordNet into the Semantic Web-compliant format RDF/OWL. Both their
procedures will be adapted in this work, as shown in section 3.1 and 3.2, followed
by a proof of concept (3.3). Because of additional expertise in the field, our cur-
rent approach intends to pursue this work by applying the restoration domain as
a use case: if the domain knowledge’s integration into the (Multilingual) Lexical
Linked Data Cloud succeeds, it will be enriched with knowledge from a broader
KOS as is provided by the ICC with its Knowledge Domains. Consequently a
link will be created other research fields for which its relevance has not yet been
2
http://www.gei.de/en/home.html
3
http://gei-digital.gei.de/viewer/
4
http://bibliothek.gei.de/en/library/curricula-workstation-textbookcat.html
5
http://repository.gei.de/
6
http://edumeres.net/
3
detected. Ideally new research questions may arise from this unexpected combi-
nation. Especially the field of textbook research with its highly multidisciplinary
character may benefit (cf. Dahlberg 2017, 2014, 120).
1.2 Preliminary Work
Dahlberg’s Information Coding Classification Dahlberg’s Information Cod-
ing Classification (ICC) (Dahlberg 2017, 2014, 1974) system follows this inten-
tion with its structure based on a model of ordering the world’s knowledge
disciplines according to ”evolution theoretic aspects” (”evolutionstheoretischen
Gesichtspunkten”). The knowledge of the world is seen as belonging to different
levels of existence (”Seinsschichten”), each structured through ”aspects”. Struc-
turing each (sub-)level this aspect-oriented view features infinite extensibility.
Its bilingual character can be seen as an additional advantage: The multilin-
gualism of the Web is a source of ambiguities where the ICC may function as a
translation tool.
The initial development of the ICC as a ”faceted classification” by Dahlberg
in the early seventies was made possible through a funding by the former Dt.
Gesellschaft fr Dokumentation e.V. (DGD)7 . Dahlberg’s intention was to estab-
lish a classification system not depending on the division into knowledge disci-
plines in order to facilitate long term usage and extensibility (De Luca/Dahlberg
2014, 3 et seq.). Her approach is based on J.K.Feibleman’s (1954) and N. Hart-
mann’s (1964) ”Schichtentheorie des Seins” (”Theory of Levels of Being”), di-
viding existence/being into layers of ”areas of being” (”Seinsbereiche”). Each
layer is structured by nine ”categorically defined aspects” functioning as ”Sys-
temstellenplan” oder ”Systemifikator” and dividing the ”areas of being” into
nine ”Subject Groups” (”Sachgruppen”) (SG) and further into nine ”Knowl-
edge Domains” (”Wissensgebiete”) (WG) (for further description cf. Dahlberg
2017, 2014, 1974, 230 et seq., 259 et seq.).
De Luca’s RDF/OWL for EuroWordNet De Luca (De Luca/Dahlberg
2014, 4) pursued the findings in the context of improving Information Retrieval
results by means of NLP techniques. He decided to take the ICC into considera-
tion in his approach of extending the classification resources WordNet Domains
(Magnini/Cavagli 2000) (WN Domains),based on the commonly used Princeton
WordNet (Miller et al. 1990, Fellbaum 1998) (PWN), and especially the mul-
tilingual extension EuroWordNet (Vossen 1997) (EWN), used in the Semantic
Web context. De Luca’s work comprises the conversion of EuroWordNet into a
Semantic Web-compliant format (De Luca et al. 2007), for which RDF/OWL
has proved to be the most useful, based on the RDF/OWL format for Prince-
ton WordNet developed in (van Assem et al. 2004). The RDF/OWL WordNet
model comprises of three main classes SynSet, WordSense and Word with re-
spective subclasses, and three relation types (for SynSet and WordSense each,
7
In 2014 it was renamed as Deutsche Gesellschaft fr Information und Wissen e. V.
(DGI). http://dgi-info.de/
4
Fig. 1. Table 1: Information Coding Classification. Survey of Subject Groups (English)
and attributes). An example is shown in figure 1. The conversion supports stan-
dardisation and hence the long term usage, as well as the EuroWordNet’s mul-
tilingualism. In (De Luca/Dahlberg 2014) we have adopted their EuroWordNet
conversion for the ICC and discussed our approach how to extend the ICC with
the EuroWordNet in RDF/OWL. This approach will be followed in the present
paper (see section 3.2), using the MultiWordNet (Magnini et al. 1994), a multi-
lingual version of the Princeton WordNet, which, as opposed to the EWN, also
adheres to the PWN’s structure and hence to the WN Domains. This way we
will be able to implement the mapping between the WN Domains and the ICC
Knowledge Domains, which at present has been generated on a theoretical level.
Fig. 2. Figure 1: RDF/OWL-EuroWordNet SynSet Example
5
2 Approach extension and Use Case
2.1 ICC Actualisation
The work’s continuation presented in this paper comprises several steps: the
first step will be the actualisation and completion of the ICC itself, namely the
integration of the fourth to sixth level. This will be achieved by completing
Dahlberg’s preliminary project ”Logstruktur” (Dahlberg 1977, 1979) and pur-
suing the work of her dissertation (Dahlberg 1974). The following tasks need to
be handled:
1. Revision: Sighting of the work done in 1974 by Dahlberg and complementing
possibly missing definitions in all divisions, English translation.
2. Actualisation/Updating: inserting newly established knowledge domains into
ICC (since the ”Vademecum Deutscher Lehr- und Forschungssttten” (1960)
used by Dahlberg for this step in 1974 does not exist anymore, a new source
signing German research institutions needs to be found; domain experts’
questioning).
3. Review by domain experts
4. compilation of defined terms in alphabetical order (Lexicon basis)
5. compilation of defined terms in systematic order (classification basis)
6. English translation
2.2 ICC and MultiWordNet conversion into RDF/OWL EWN and
mapping
This part applies the conversion method from (De Luca et al. 2007) and (De
Luca/Dahlberg 2014, 8), first format adaptation:
1. schema extension/adaptation:
– ICC and MultiWordNet analysis: specific requirements and format adap-
tation
– RDF/OWL EWN format adaptation for ICC and MultiWordNet
2. The second step will be to apply the conversion method developed in (De
Luca et al. 2007) and again proposed in (De Luca/Dahlberg 2014) in order
to have the ICC and MultiWordNet data in an adequate format which allows
the theoretical and practical mapping onto the WN Domains:
– theoretical mapping: map the ICC knowledge domains onto the WN
Domains, based on the initial approach done in (De Luca/Dahlberg 2014,
6), taking into account the ICC updates from step one
– practical mapping: After the two ontologies have been mapped on a the-
oretical as well as on a schema level, the actual data integration between
the ICC and the WordNet Domains via the MultiWordNet will be possi-
ble. Again this procedure will be done according to the approach in (De
Luca/Dahlberg 2014, 8 et seq.): enlarging the MultiWordNet coverage
with the ICC generic terms of the Knowledge Domains, declaring every
ICC generic term as a class (owl:Class) and every underlying term as a
6
subclass (rdfs:subClassOf), eventually supplementing every class (of the
ICC top ontology) with a language description (e.g. xml:lang=”en”), in
order to add it to the correct set of language files of the MultiWordNet.
2.3 Proof of concept: Restoration Domain as Use Case and
Information Retrieval Tasks
As a proof of concept, we decided to add a third step to link the restoration do-
main ontology intended for document indexing purposes: the chosen restoration
terminologies have been established especially for the SemRes project and used
only on an experimental level.
The integration is based on the approach in (De Luca et al. 2007) and will be
done on a manual level: after determining the correct SynSet in the WN-ICC-
Extension, the domain ontology’s top concept will be added as a hyponym of the
SynSet8 . For this converting the domain ontologies into the same RDF/OWL
format is necessary9 .
Eventually we will be able to perform Information Retrieval tasks with the
converted KOS, which will test the newly integrated ICC top ontology for In-
formation Retrieval purposes. We imagine use cases, in which Information Re-
trieval is done in the restoration domain, searching for the semantic concept
”democracy”. Through the newly established links between restoration domain
ontology and the ICC Knowledge Domains via MultiWordNet, information on
the ”democracy” concept may also be found in the textbook research domain,
where the concept would be used in the context of education or art history.
This way, abstraction of concepts, integrated into the available Lexical Linked
Data Cloud, will be facilitated, and the discovery of relations between different
domains will be allowed.
3 Conclusion and future work
In this paper we have shown our approach how to establish a ”Lexikon der
Wissensgebiete” by pursuing the work of Dahlberg (1974, 1977, 1979). We then
discussed how to bring this knowledge database into the lexical linked data cloud
by converting it into RDF/OWL to use it as a Top Ontology within the Lexical
Resource WordNet. This step is based on the EuroWordNet conversion into
RDF/OWL, presented by De Luca et al. (2007). By integrating the restoration
8
”After having found the correct SynSet, we merged manually the complete converted
domain ontology under the appropriate hyperonym (SynSet). In this case we could
enlarge the EuroWordNet coverage with domain-specific terms.” De Luca et al. 2007,
9.
9
”The first step before including the domain ontologies in the new EuroWordNet
OWL hierarchy was to convert these in the same OWL format [] merging methods for
including these domain-ontologies to the EuroWordNet Owl representation [] domain
ontology is then added to the generic one, directly under its new hyperonym.” De
Luca et al. 2007, 9.
7
domain ontology as a use case we can evaluate the usefulness of the enriched
lexical resource for Information Retrieval or Query expansion tasks.
Further possible work comprises the connection of additional domain ontolo-
gies, e.g. archaeology, to evaluate the usefulness of the produced and enriched
lexical linked data knowledge repository in Digital Humanities research scenar-
ios. We imagine this to offer a general improvement for Information Retrieval
within the Digital Libraries world.
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