=Paper=
{{Paper
|id=None
|storemode=property
|title=Automatic Classification of Scientific Records using the German Subject Heading Authority File (SWD)
|pdfUrl=https://ceur-ws.org/Vol-912/paper3.pdf
|volume=Vol-912
|dblpUrl=https://dblp.org/rec/conf/ercimdl/WartenaS12
}}
==Automatic Classification of Scientific Records using the German Subject Heading Authority File (SWD)==
https://ceur-ws.org/Vol-912/paper3.pdf
Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
Automatic classification of scientific records
using the German Subject Heading Authority
File (SWD)
Christian Wartena and Maike Sommer
Hochschule Hannover - University of Applied Sciences and Arts
Department of Information and Communication
Expo Plaza 12, 30539 Hannover, Germany
Christian.Wartena@fh-hannover.de
Maike.Sommer@stud.fh-hannover.de
Abstract. The following paper deals with an automatic text classifica-
tion method which does not require training documents. For this method
the German Subject Heading Authority File (SWD), provided by the
linked data service of the German National Library is used. Recently
the SWD was enriched with notations of the Dewey Decimal Classifi-
cation (DDC). In consequence it became possible to utilize the subject
headings as textual representations for the notations of the DDC. Basi-
cally, we we derive the classification of a text from the classification of
the words in the text given by the thesaurus. The method was tested by
classifying 3826 OAI-Records from 7 different repositories. Mean recipro-
cal rank and recall were chosen as evaluation measure. Direct comparison
to a machine learning method has shown that this method is definitely
competitive. Thus we can conclude that the enriched version of the SWD
provides high quality information with a broad coverage for classification
of German scientific articles.
1 Introduction
Subject classification is one of the major pillars to guarantee accessibility of
records in large digital libraries. One of the worldwide most common classifica-
tion systems is the Dewey Decimal Classification (DDC). The DDC is a universal
classification system aiming at representing the entire knowledge of the world. It
is used in more than 135 countries and translated into over 30 languages. More
than 60 countries use the DDC even for their national bibliography. Apart from
the worldwide use the DDC has a second strength: It is administrated by the
Decimal Classification Editorial Policy Committee at the Library of Congress.
Thus it is updated and developed continuously ([12]).
Classifying records according to DDC is a task that requires carefully reading
and understanding of the abstracts and other available meta data as well as a
This work is partially based on the Bachelor thesis of Maike Sommer ([17]).
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
detailed knowledge of the DDC class hierarchy. In a number of projects classi-
fiers were built using machine learning techniques ([20], [19]). These approaches
are problematic because the DDC-classes are very fine grained. Even in very
large repositories, for most classes there are not sufficient training data. Thus a
classification can only be made on the highest levels of the DDC hierarchy and
even then the sparsity of data poses still a problem for some of the classes ([19]).
A second problem is the dependency on training data. Especially, the data that
are classified have to be comparable to the data that were used for training. E.g.
if the collection to be classifies contains other text types than those used for
training, the results might be worse than expected.
The basic principle of text classification based on machine learning is as
follows. In the training phase words are given weights indicating how strong
they characterize a certain class. During classification these weights are used
to guess the most likely class for a text. Instead of determining weights in a
training phase we can use a dictionary or thesaurus, if it contains information
on the relation between words and the target classes, in our case the classes
from the DDC. Recently, a large number of relations between subject headings
of the German Subject Heading Authority File (Schlagwortnormdatei, SWD) and
DDC-classes have been published ([4], [9]). Since most subject headings consist
of just a single word or a very short phrase, we can use the SWD as a large
lexical resource with a very broad coverage. Now, basically by counting the links
of the subject headings found in a text to the DDC-classes we can predict the
DDC-class for the text. The disadvantages of this approach are manifest: Weights
are just 0 or 1, without any information how indicative a word is for a certain
class. Furthermore, only the weights of the words are used and no dependencies
between words can be modeled. The method has, on contrary, also the advantage
that we need no training data and we directly can classify documents in domains
that we did not see before. The success of this approach depends crucially on
the quality of the thesaurus used. The main contribution of this paper is, that
we show that the SWD is a vary valuable source of information in this respect.
In the following we will describe our approach in more detail and present re-
sults on the classification of the German language records from 7 repositories of
different German universities. We compare our results with the results given by
the Automatic Classification Toolbox for Digital Libraries (ACT-DL) from the
University of Bielefeld (http://clfapi.base-search.net/doc/index.html),
that uses a state-of.the-art machine learning approach. We show that the results
are comparable in terms of mean reciprocal rank and in most cases better in
terms of recall. The first measure is important with regard to fully automatic
classification, the second measure is especially important in an interactive sce-
nario in which the algorithm provides suggestions to a librarian.
The remainder of this paper is organized as follows. In section 2 we discuss
related work. In section 3 we present our approach. In section 4 we describe the
data we have used in our experiment, the results of which are given in section 5
We conclude with a discussion of results (section 6) and outlook to future work
(section 7).
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
2 Related Work
Waltinger et al. ([19]) treat exactly the same problem that we discuss in this
paper, namely classifying English and German scientific abstracts into high-
level DDC classes. They use a state-of-the art machine learning approach for
text classification. Below we will compare our results of the ontology driven
approach directly with the results obtained by their classifier, that is publicly
available as a web service.
Various studies consider document labeling or classification with the labels
of an ontology, using lexical and structural information from that ontology. Ba-
sically, occurrences of ontology concepts in the text are counted and in some
manner the information is aggregated to determine the most central or impor-
tant concepts. Usually these approaches require enrichment of the ontology with
additional lexical information, in many cases obtained from WordNet. Examples
of such approaches are [18], [14] and [7].
Another approach to using ontologies for text classification is to enrich the
representation of the text with features derived from the ontology, like hyper-
nyms or concept labels before applying the classification algorithms. E.g., Scott
and Matwin ([16]) add WordNet hypernyms and Bloehdorn and Hotho ([3]) add
hypernyms from Wordnet and other ontologies to the representation of the text.
The latter authors also try out various disambiguation strategies for words that
potentially represent more than one ontology concept. Improvements over the
baseline using only the words from the text are in both cases not very convincing.
Addis et al. ([2]) consider text classification rather as a two step process. In
the first step WordNet concepts (synsets) are extracted. In the second phase an
existing mapping from synsets to DDC-categories is used to compute a DDC-
classification for the text. However, the authors do not consider this process
as their final classifier, but use it only to create text collections to train sta-
tistical classifiers on. The two step approach is treated more systematically by
Chenthamarakshan et al.([5]), who explicitly distinguish between the process of
finding representative concepts on the one hand side and learning a mapping
from concepts to document classes on the other hand side. These approaches
differ not only in the perspective on the task from those mentioned in the previ-
ous paragraph. They are also different because they do not add features to the
simple word vector model, but replace the original representation.
In our approach we consider classification as a two step approach as well.
Thus the main contribution of this paper is not the method presented, but rather
the investigation in the potentials of the German Subject Heading Authority
File, that was, to the best of our knowledge, not used for automatic classification
before. Since the results turn out to be very competitive, the proposed method
might also have practical value for application in libraries.
3 Approach
In our thesaurus based approach, the most relevant Dewey class for a text is
determined by the Dewey classification of the words in this text according to the
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
thesaurus. In our case the thesaurus is the German Subject Heading Authority
File (SWD) for which all terms have been related to Dewey classes.
In order to find all relevant words we stem all words in the text. To be sure
that only relevant words are found we restrict our search for thesaurus terms
to nouns only. The text analysis is implemented as a GATE pipeline ([6]). For
stemming and part-of-speech tagging we use the TreeTagger ([15]). Search for
thesaurus terms is implemented by Apolda ([21]).
In the next phase we can determine the class of the text on the basis of the
identified occurrences of thesaurus terms. For this phase we keep only unam-
biguous words, since only these terms give a clear indication of the topic of the
text. Usually enough unambiguous terms remain to determine the topic of the
text. We consider a word as unambiguous if the word occurs as the label of only
one subject in the thesaurus, or if the word is the preferred label of exactly one
subject. E.g. the word Student occurs 9 times as an alternative label for subjects
like Studentenwohnheim (student accommodation) or Auslandsstudium (study
abroad). However, there is one subject that has Student as its preferred label.
Thus we treat Student as a non-ambiguous term representing that subject. The
word Untersuchung (investigation) in contrast is found 2 times as an alternative
label but never as a preferred label. Thus this word is not considered in the
following steps. In this way many very general terms are filtered out.
Once all subjects have been identified we count the Dewey classes they are
related to. In the (enriched) SWD each word is related to one or more Dewey
classes via an anonymous node. For each relation a confidence of correctness
is given by an integer between 1 and 4. For our purposes we ignore all links
with a confidence level of 1. Given a (non-ambiguous) term occurrence t we let
ddc(t) be the set of all DDC-classes that t is related to with a confidence level
greater than 1. Most words are related to very specific class in DDC. In order
to aggregate occurrence information on a higher level in the DDC-hierarchy we
denote for each class c in the DDC-System the broader class at the n-th level
as cn . Since the DDC-system is a strict hierarchy cn is uniquely defined for each
class with a depth smaller than n. E.g. if c is the class 342.0684 then c2 is 340.
Now we can define the contribution of a term t to each DDC-class c as
|{ci ∈ ddc(t) | cni = c}|
w(t, c) = (1)
|{ci ∈ ddc(t)}|
where n is the hierarchy level of c. Considering a text T as a set of term occur-
rences we define the weight of a class c for T as
w(T, c) = w(t, c). (2)
t∈T
This gives us almost a ranking of DDC-classes for a text T . Only in case two
classes have the same weight we need to specify their ranking. In these cases
we order the classes by the order of their first occurrences in the text, where an
earlier occurrence implies a higher rank.
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
4 Data and experimental setup
The experiment we present here was enabled by the results of the CrissCross
project conducted by the Cologne University of Applied Sciences (Fachhochschule
Köln) in collaboration with the German National Library (Deutsche National-
bibliothek). In this project a concordance between the German Subject Heading
Authority File (Schlagwortnormdatei, SWD) and the DDC was constructed.
In other words the subject headings were mapped to notations of the DDC.
The SWD is a universal indexing language based on rules, namely the rules for
the subject catalog (Regeln für den Schlagwortkatalog, RSWK) and the prac-
tice rules for the RSWK and the SWD. In contrast to the DDC there are not
that many relations between the subject headings. In accordance with an un-
published study from 2004 almost 87 % of the subject headings do not have
associative relations. Furthermore 34% have neither associative nor hierarchical
relations. The enrichment of the SWD with DDC notations is helpful in struc-
turing the SWD because it generates hierarchical, equivalence and associative
relations through similar DDC notations. We already mentioned that there were
not were not many relations between the subject headings before the Criss-Cross
project ([11]). Thus a subject heading can be interpreted differently. The project
group mostly mapped one subject heading to several DDC notations ([10]). Fur-
thermore, the meaning of a subject heading is often very specific. Therefore the
mapped DDC notations are also very specific, which means mappings to a deep
hierarchy level. Hence this is called deep level mapping ([11]). In our experiment
we only wanted to gain notations up to the second hierarchy level, that can
easily be obtained through the DDC hierarchy as explained above. Furthermore,
there is much variance to what extent a subject heading fits into a DDC class.
To express this distinction, the project group invented four confidence levels (de-
grees of determinacy) with 1 for the lowest and 4 for the highest congruency. As
aforementioned we disregarded all first level relations, because these mappings
point to DDC notations with only a small thematic intersection ([1]).
The released version of the enriched SWD (https://wiki.d-nb.de/download/
attachments/34963694/SWD_s_rdf.zip) has about 188,000 concepts linked to
51,748 DDC-classes. The concepts have preferred and alternative labels. These
labels are however labels of subject headings and not intended to be used as
a lexical resource for analyzing texts. Some concepts have labels that are very
unlikely to appear in running texts. However, in many cases the terms are single
words or small phrases that will appear in normal texts.
More problematic are however concepts that have labels that will occur in
many texts for which the concept is not relevant. This can be the case with
words that have a meaning that is related to some subject area but that also
can be used in a more general way. E.g. the word Zusammenhang can be used
in a general way, meaning context or connection, but it is also the alternative
label of Zussamenhang in einer Mannigfaltigkeit (Connectedness in a manifold)
that is mapped correctly onto the Dewey class 516.35 (Algebraic geometry).
Another class of words causing problems in a similar way are the homographs
and homonyms. E.g. the abbreviation ALS (for the disease amyotrophic lateral
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
sclerosis) is a homograph for the very frequent conjunction als (as). A number
of these homographs can be filtered out, because the different meanings corre-
spond to different parts of speech. As mentioned before we only consider words
from the text that were tagged as noun. Another example is constituted by the
word IM that is an alternative label of the term Spitzel (spy), since it is the
abbreviation of Informeller Mitarbeiter (informal staff), especially for the intel-
ligence department of the GDR. Its homograph im is a highly frequent word
that is the contraction of the words in dem (in the). Furthermore, many aux-
iliaries and function words are included in the category linguistics. In order to
avoid problems with these words we removed all concepts from the class 435
(German grammar) except for the subject headings rational, irrational and Glo-
ria because they are mapped into a second DDC class apart from 435. Also
the subject heading Grammis was not removed because it is not a stop word
but the abbreviation for grammatical information system (Grammatisches In-
formationssystem des IDS (Institut für Deutsche Sprache, Institute for German
language)) Additionally we removed all concepts with a question mark (”?”) as
preferred label and the following alternative labels: im and in (as abbreviation
of intelligentes Netz (intelligent net) as an extension of a telephone network).
After that we could use the subject headings as textual representations for the
DDC-classes. In sum 314,287 preferred and alternative labels could be used as
textual term representations.
Table 1. OAI-Metadata repositories used in this paper. From each repository all
records of publications in German language with an abstract available at the date
of retrieval were used.
URL University #records date of retrieval
http://opus.bsz-bw.de/ Hanover UAS 271 2012-05-27
fhhv/oai2/oai2.php
http://opus.bibl.fh-koeln. Cologne UAS 254 2012-06-13
de/oai2/oai2.php
http://opus.bsz-bw.de/ Frankfurt am Main UAS 120 2012-06-13
fhff/oai2/oai2.php
http://opus.kobv.de/ TU Berlin 2036 2012-06-13
tuberlin/oai2/oai2.php
http://opus.bsz-bw.de/ Univ. Hildesheim 97 2012-06-13
ubhi/oai2/oai2.php
http://www.opus-bayern.de/ Univ. Regensburg 790 2012-06-15
uni-regensburg/oai2/oai2.
php
http://opus.bsz-bw.de/ Freiburg 258 2012-06-14
phfr/oai2/oai2.php Univ. of Education
For testing the effectiveness of the proposed classification strategy we have
used in the first place the repository of the Hochschule Hannover - University
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
of Applied Sciences and Arts. This repository supports the Open Archives Ini-
tiative Protocol for Metadata Harvesting ([13]). We have classified metadata
records of this repository using different fields, like title, abstract and keywords
at the first and second level of the DDC-hierarchy. In most realistic scenarios one
will have the title and the abstract of a publication that has to be classified, but
not keywords. Thus we concentrated on classification using title and abstract.
Besides the repository of the Hochschule Hannover, we used 6 more repositories.
The repositories were chosen on the basis of the presence of an OAI-PMH inter-
face, the size of the repository and the availability of the required metadata and
classification. We selected three repositories from universities (among which one
technical university), three universities of applied sciences and one university of
education. Details of the repositories are given in Table 1.
Since the SWD is a German resource, we are only interested in publications in
German. Thus we have selected from the repositories only those publications that
are marked explicitly as written in German. However, most German publications
have German and English abstracts. We did not include a language detection but
simply assumed that the first abstract is the German one. We did not find any
counterexample. The universities have an emphasis on fundamental research and
are internationally oriented. Hence they publish mainly in English. The majority
of their German publications are PhD-theses. In contrast the universities of
applied sciences (UAS) are regional oriented and have an emphasis on knowledge
transfer. Moreover, they usually don’t have PhD-Students. Thus, they have a lot
of publications in German that are intended to inform professionals in industry
about new research and developments. The Universities of Education have a
position in between with PhD-theses but also a lot of other German publications.
Each of the repositories mentions a subject area of the publication. For all
repositories this is a DDC class at the second hierarchy level. However, some
of the repositories use the class 004 for computer science. We did not take this
exception into account. All records with this label consequently will have a recall
and mean reciprocal rank of 0 for every classification method.
5 Results
All analyzed records provide a subject area that is in fact a second level DDC
notation. It has to be noted that in many cases there is more than one possible
label that could be regarded as true and a more or less arbitrary choice had to
be made by the annotators. In fact labels closely related to the ground truth
could be considered as correct as well ([8]). Furthermore, on closer inspection of
the results, it turns out that in some cases of mismatch, the predicted label is
the correct one and the label given by the repository was wrong ([17]). In the
following we will nevertheless use these labels as the ground truth and consider
only exact matches as being correct.
Since we consider assignment of DDC Notations as a classification task, in
which each record should be assigned to exactly one category we have to ob-
serve the results for each record and not for each category, like one would do
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
Table 2. Mean reciprokal rank (MRR) and recall at 5 at first and second DDC level
for SWD based classification of OAI Metadata from the SerWiss repository of the
Hochschule Hannover using different fields and two classification methods, sci. the
SWD-based classification and the ACT-DL classification service.
SWD Based ACT-DL
Fields MRR rec@5 MRR rec@5
title + abstract (DDC level 1) 0.68 0.89 0.67 0.90
title + abstract (DDC level 2) 0.48 0.66 0.39 0.37
title + keywords (DDC level 2) 0.61 0.76 0.32 0.39
title + abstract + keywords (DDC level 2) 0.61 0.77 0.39 0.47
in a retrieval setting. Thus the evaluation presented here differs from the one
used in [19] who use the retrieval perspective. Since the algorithm produces a
ranked list of results, we use mean reciprocal rank as an evaluation measure.
Automatic classification might be used in a setting where a subject librarian is
given suggestions for manual classification. Here it would be important that the
correct label is always among the top 5 or top 10 results. Thus we also consider
the recall at the fifth position in the ranked list (recall@5). Note that for each
individual record the recall@5 is always 0 or 1.
Table 2 gives the results for classification of records form the Hochschule
Hannover using different fields for both the ACT-DL classification service and
the method presented in this paper. Though ground truth labels are given at
the second level of the DDC-hierarchy, we can of course also evaluate the results
at the first level. These first level results are given on the first line of the table.
Table 3. Mean reciprocal rank (MRR) and recall at 5 at second DDC level for SWD
based classification of OAI Metadata of records from 7 German OAI-repositories using
two classification methods, sci. the SWD-based classification and the ACT-DL classi-
fication service.
SWD Based ACT-DL
Repository MRR rec@5 MRR rec@5
Hanover UAS 0.48 0.66 0.39 0.37
Cologne UAS 0.32 0.44 0.35 0.39
Frankfurt UAS 0.55 0.75 0.49 0.62
TU Berlin 0.41 0.61 0.59 0.66
Univ. Hildesheim 0.25 0.39 0.25 0.29
Univ. Regensburg 0.61 0.80 0.65 0.72
Freiburg UE 0.53 0.75 0.33 0.36
Of course the results using the keywords gives the best results but is of least
practical relevance for a library that wants to speed up the process of metadata
generation for new publications. In the realistic situation only the abstract is
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
available, or author provided keywords that might be of less quality than the
subject headings assigned by a librarian with in-depth knowledge of the subject
authority file. Thus in Table 3, where we compare results for 6 more repositories,
we use only title and abstract for classification. All the differences between the
two methods are significant at the level of 0.001 according to the Wilcoxon signed
rank test.
Finally, we have compared the results for different publication types. These
results are given in Table 4.
Table 4. Mean reciprocal rank (MRR) and recall at 5 at second DDC level for SWD
based classification of OAI Metadata for 6 most frequent publication types from 7
repositories.
SWD Based ACT-DL
Publ. type #records MRR rec@5 MRR rec@5
PhD Thesis 2503 0.47 0.66 0.63 0.70
Master thesis 277 0.32 0.44 0.37 0.41
Essay 195 0.59 0.75 0.29 0.36
Monograph 176 0.46 0.62 0.43 0.51
Festschrift 106 0.43 0.76 0.38 0.40
Lecture 84 0.40 0.96 0.03 0.06
6 Discussion
The results of the SWD based approach are similar to those given by ACT-DL,
which is rather surprising given the simplicity of our approach. Especially the
recall@5 is very good for the SWD based approach as compared to the machine
learning method: For 6 out of 7 repositories the recall@5 was even better. The
mean reciprocal rank is 3 out of 7 cases better, in 1 case the same and in 3 cases
worse. This shows that our method is rather successful in getting the correct
label among the best 5 candidates but has difficulties to decide which one to put
on top. A detailed analysis of a small subset shows that in many cases the first
and the second result have the same weight and the ordering is arbitrary. Here
machine learning techniques considering statistical relations between words of
different categories or at least using a priori probabilities for the categories could
improve the results.
The results split up for different publication types are probably most inter-
esting. Especially, the SWD-based approach is able to outperform the machine
learning approach for the less typical publication types. It is likely that there
were not many examples of these publication types in the training data for the
ACT-DL classifier, while at the same time there is a considerable difference in vo-
cabulary between the publication types. Thus, it should be fairly easy to adjust
the classifier by including additional training documents to get better results for
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Proceedings of the 2nd International Workshop on Semantic Digital Archives (SDA 2012)
these publication types as well. Nevertheless it shows the weakness of the ma-
chine learning approach: it is extremely dependent on the proper composition of
the training data. The thesaurus based approach on the other hand, might not
reach the best possible results, but is independent of training.
The quality of results that can be achieved with the thesaurus based approach
of course depends on the coverage and quality of the thesaurus. In the work
presented here we could show that the enriched version of the German Subject
Heading Authority File (SWD) is a high quality resource for classifying German
scientific records into DDC-classes.
7 Conclusion and future work
We have shown that the SWD with the mapping of SWD-subject headings to
DDC classes provides a very valuable resource that can be used for classification
of scientific records. With basic methods we could already achieve results that are
comparable to results from state-of-the-art machine learning algorithms. There
are various possibilities for improvement. In the first place, the SWD is a file of
subject headings. Especially for complex or ambiguous concepts subject headings
are often formulated in a way that might never be found in a running text.
Thus, lexical enrichment might improve the results. Furthermore, we have simply
counted the links to DDC classes. This works only, to some extend, if the number
of terms per class is well balanced. In general, it does not have to be that case
that class from which the most terms are found, is also the most likely class for
the text. Here a machine learning approach as proposed by [5] could be used.
Another issue for further research is the reason why for some repositories the
SWD-based approach is better, while for others the trained classifier is superior.
The difference can partly be explained by the different distribution of text types.
The reason might also be hidden in some properties of the repository but also in
the policies and habits of the libraries that assign the labels that we have used
as ground truth.
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