Enhanced Web Services in Digital Libraries using Lexical Chaining
Ulli Waltinger
, Alexander Mehler
and Gerhard Heyer
Text Technology, University of Bielefeld, Universitätsstraße 25, 33615 Bielefeld, Germany
Institute of Computer Science, NLP Department, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany
Keywords: Topic Tracking, Topic Structuring, Topic Labelling, Social Tagging, Digital Library, Wikipedia, Lexical
Network, Lexical Chaining.
Abstract: This paper proposes a web-based application which combines social tagging, enhanced visual representation
of a document and the alignment to an open-ended social ontology. More precisely we introduce on the one
hand an approach for automatic extraction of document related keywords for indexing and representing
document content as an alternative to social tagging. On the other hand a proposal for automatic classifica-
tion within a social ontology based on the German Wikipedia category taxonomy is proposed. This paper
has two main goals: to describe the method of automatic tagging of digital documents and to provide an
overview of the algorithmic patterns of lexical chaining that can be applied for topic tracking and –labelling
of digital documents.
Taxonomies and Collaborative Tagging
The phenomenon of the web 2.0 can be directly
associated to web technologies such as search en-
gines, web mining, meta-standards but first and
foremost with the socialisation and collaboration of
internet users. An area which has grown in popular-
ity particularly in the blogsphere and digital library
services is collaborative tagging. In this scenario,
weblogs, web-services and document repositories
provide documents, bookmarks and multimedia
content are organized by assigning keywords or tags
by collaborating users. Interestingly, it turns out that
this process is highly predictive showing that there
are general principles of collective information or-
ganization. However the action of tagging content
always is a process of a subjective decision. It is
neither exclusive nor necessarily hierarchical. One
can introduce keywords without knowledge about
whether and in which context that label has been
used by others. Moreover, the new introduced tag
might also be a reference for other users to describe
their content. Clearly, collaborative tagging reflects
the dedication of users in web communities, but
common problems of natural language processing
also appear in collaborative tagging. These are:
wrong notation (keywords are written wrongly)
polysemy (ambiguity of tags)
synonymy (sense-related tags without being an-
notated that way)
missing context views of the socially accepted
usages of tags
missing overviews of tag systems
There are many web technologies that do assist users
in assigning related tags to content units.
Most commonly the representation of tag clouds,
i.e., a weighted list of user generated content-tags,
which indicate the most frequently, used classifiers.
By means of such clouds, user’s are not only in-
spired but also swayed to use already assigned
terms. Moreover, several web services implement
tag-recommendation systems which indicate previ-
ously assigned or shared tags by the user in sequence
patterns during the action of tagging. On the other
side, (Golder et al., 2006) have shown that the dis-
tribution of tags stabilises on base of a common
denominator, that is, a shared vocabulary. Therefore,
some users apply a wide range of different tags to
their content, some introduce only a few, but it can
Mehler A., Waltinger U. and Heyer G. (2008).
TOWARDS AUTOMATIC CONTENT TAGGING - Enhanced Web Services in Digital Libraries using Lexical Chaining.
In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 231-236
DOI: 10.5220/0001527502310236
be observed a stable pattern in tag proportions with-
out global control.
Social tagging produces some sort of a tag-
taxonomy. In contrast to existing ontologies, e.g.,
the tree-like Dewey decimal classification, social
tagging induces graphs which are constantly chang-
ing. Furthermore, folksonomies do not force unam-
biguous categorizations, but realize multi-label clas-
sifications. A prototypical example is the category
system of the Wikipedia (Voss, 2006) which is an
open-ended social ontology enhanced by a commu-
nity not only by publishing and interlinking of arti-
cle, but also by enabling user to categorize docu-
ments (Gleim, Mehler, 2006).
This paper proposes a web-based application
which combines social tagging, enhanced visual
representation of a document and the alignment to
an open-ended social ontology. More precisely we
introduce an approach for automatic extraction of
topic labels for indexing and content representation
as an add-on to social ontologies. That is, we per-
form automatic document classification in the
framework of a social ontology based on the
Wikipedia category taxonomy. This paper has two
main goals: to describe the method of automatic
tagging of digital documents and to provide an over-
view of the algorithmic patterns of lexical chaining
that can be applied for topic tracking and -labelling
of digital documents. Thereby, we first explain the
general architecture of the system in Section 2. Then
we present a formal model of the used lexical chain-
ing algorithm in Section 3. In Section 4, we outline
the alignment with the Wikipedia category system.
Finally, we give a conclusion and prospect future
The method proposed in this paper belongs to the
domain of content classification in special the tag-
ging of content though meta-information and the
alignment of documents on a social ontology.
(Braun et al., 2007) presented an application
(SOBOLEO) on alignment of collaborative tagging
to a light-weighted ontology. This approach enables
users to add hyperlinks to an online-repository – so
called ‘social bookmarks’ – by assigning tags to
hyperlinks. Furthermore, each bookmark can be
categorized by referring to a terminological ontol-
ogy. The employed ontology can be specialised by
assigning new concepts. In this case both, tagging
and categorization of content has to be done manu-
ally. Contrary, our focus is set to an automatic –
none manually - approach of tagging and categoriza-
(Mika, 2005) presented an application for the extrac-
tion of community-base light-weighted ontologies
from web-pages. In special creating actor-concept
ontology by generating associations between an
actor (e.g. person) and a concept (e.g. label). This is
done by submitting a search query, combining the
two terms, and measuring the resultant page count.
This approach tends to be similar to the classical
lexical chaining approach, using a lexical network
(in this case a search engine) as a resource for gen-
erating associations between two terms. However an
integrated structure and content-based text model is
left out by using only already assigned tags from
The main concept towards automatic content tagging
and topic tracking is an integrated structure and
content-based text model approach. This means in
first place the task of tracking semantically related
tokens based on a lexical reference system is com-
bined with a detailed structure analysis of text. The
idea behind this is that each content element of a text
(content and structure) is always semantically re-
lated to another segment in the same text. Therefore
we can span associations between tokens, sentences,
paragraphs and divisions based on their semantic
relatedness. This is done by introducing a Generic
Lexical Network Model exemplified by using a snap-
shot of the German Wikipedia-Project.
In addition an alignment to an existing ontology is
computed by normalizing, labelling and categorizing
topic chains. Generally speaking, the application
procedure can be subdivided into three coordinated
main modules
(see Figure 1) which provide an inte-
grated structure- and content-based text model for
topic tracking and automatic content tagging:
1. analysis of logical document structure
2. lexical content analysis and term extraction
3. ontology alignment and topic labelling
WEBIST 2008 - International Conference on Web Information Systems and Technologies
Figure 1: System Architecture.
3.1 Analysis of Logical Document
A fundamental requirement of this module is to
process a wide range of different input documents.
Therefore Plaintext, PDF-, Open Office-, Word- and
(X) HTML documents must be automatically analyz-
able. The possibility to process documents of a wide
range of formats is indispensable from the point of
view of digital libraries. We meet this demand by
having integrated mapping routines for all these
formats. Once having extracted the content of an
input document a transformation to a XML-Format
is deployed. All content is converted into the Corpus
Encoding Standard
(Ide et al., 1998) which has been
designed for mapping Logical Document Structures
(Power et al., 2003) of large corpora in language
engineering. We provide this by extracting section
(title, sub-title, header, body…), paragraph and sen-
tence structures as well as images. As a result, each
input document is mapped onto a tree-like represen-
tation which can be accessed for structure-oriented
retrieval. Once the logical document structure has
been extracted, lemmatization of lexical content is
deployed. The process of determining the lemma
information for an extracted token is needed in order
to retrieve information out of a lexical type network.
Therefore, we developed an interoperable lemma-
tizer. It is based on the Morphy system (Lezius,
2000) which integrates a morphological analysis
with part-of-speech tagging in a single package. We
used a German edition of the Wikipedia as well as a
ten years release of the German newspaper ‘Süd-
deutsche Zeitung’ to extract the morphological in-
formation of Morphy. As a result, we generated a
lexicon of more than 3.7 million word forms which
are currently the basement of our tagging-
application. The lemmatizer is used to annotate
lexical information within input documents in the
CESDOC-format. In addition token positions within
sentences and paragraphs are annotated
(see Figure
. These so called corresponding ‘c’ attributes mark
the position of the element in the XML DOM tree.
As a result a hierarchical CESDOC-XML-Document
is generated including logical document structure
and lexical information.
<TEXT id=’TEXT1’>
<T c=’1’>
<L p=’NN’>Datum</L>
<T c=’2’>
<L p=’SZ’>:</L>
Figure 2: A Snapshot of a CESDOC-XML document.
3.2 Lexical Content Analysis
The second module (see Figure 1) of our application
is concerned with lexical content analysis. The idea
behind our lexical chain is the assumption that se-
mantically related tokens of a document do occur
within a restricted area of text segments (Halliday,
Hassan, 1976). Following this idea, a token at posi-
tion one in paragraph one tends to have a higher
probability in being semantically related to a token
in the same paragraph than with a token of the last
paragraph of that document. Since we have a model
of an ordered hierarchy of content objects, we are
able to link any pair of tokens within instances of
certain constituent types of the logical document
structure. Thus, we can implement logical distances
not only in terms of the numbers of tokens in-
between, but also in terms of, e.g., intermediary
paragraphs, sentences etc. In order to classify a con-
nection between a pair of token as a lexical edge, an
external resource for lexical chaining is needed. This
is provided by the usage of a type network as a
model of a terminological ontology. Semantic tax-
onomies such as WordNet (Fellbaum, 1998) provide
a rich source of lexical knowledge for text and web
mining, but are limited in the sense that they do not
cover special vocabularies as they are typical for
scientific texts to be managed by digital libraries.
Thus, we decided to use an open-ended social ontol-
ogy as a resource for lexical chaining. In this case,
the German release of Wikipedia.
TOWARDS AUTOMATIC CONTENT TAGGING - Enhanced Web Services In Digital Libraries using Lexical Chaining
More specifically, Wikipedia article, categories and
portal documents have been used to induce vertices,
whereas hyperlinks induce edges. In special, vertices
are typed as articles, portals or categories and edges
are labelled as, e.g., hyperonym of (in the case of a
link from a superordinate to a subordinate category),
article of (in the case of a link from an article to a
portal) or as an association (in the case of a link
between two articles). As a result we get a lexical
network which spans the reference plane of lexical
edges as the resource for computing lexical chains.
More specifically, rating pairs of tokens on basis of
their semantic relation equals to their minimal dis-
tance in the referred terminological ontology (Mor-
ris, Hirst, 1991). By that, lexical chains can be de-
fined as graphs spreading over an inclusion hierar-
chy of text. Though lexical chains can be computed
by the following algorithm:
foreach token T of paragraph P
foreach token T´ of paragraph P +/-
paragraph distance parameter X
compute shortes-path as graph-
distance D(T, T`) within lexical
network N;
if ( pair D(T,T`) < network distance Y )
build lexical chain L;
In general, the time complexity of chaining algo-
rithms is high as they rely on computing shortest
paths which is of order O(|V| |E| + |V|
log |V|) (as,
e.g., the Johnson all pairs shortest path algorithm
(Johnson, 1977). There also exist proposals for a
chaining algorithm in linear time (Silber, McCoy,
2002). However, this approach cannot be applied to
the Wikipedia as it misses the rich type system of
WordNet utilized by Silber & McCoy. Thus, we
alternatively explored the small world nature of the
wiki graph (Zlatic et al., 2006; Mehler, 2006) and
constrain the maximally allowed path length to a
value < 3 where a distance of three links corresponds
to the average geodesic distance in wiki graphs.
As a consequence, shortest paths are efficiently
computed as they are reduced to a simple look-up
mechanism. More specifically, we reduce time com-
plexity to an order of O(|V||E|) supposed that the
maximally allowed path distance in the terminologi-
cal ontology is one. The reason is that in the worst
case we have to consider all pairs (v,w) of lemma
) where for each vertex v we have to examine
on average |E|/|V| edges. Next, all lexical chained
pairs of tokens are clustered in order to get so called
meta-chains describing the content of the input
document. Depending on the used lexical-distance
parameter, e.g. P, we get a snapshot of the content of
the input document as in
Figure 3.
So far, we have explored document structure,
lemmatized all lexical content and have put all lexi-
cal items which are semantically related in terms of
Wikipedia into the same lexical chain. As an output
we get a set of such chains where the largest thereof
represents the main document content. It can be
accessed to further process the input document and
to perform a semantic search, that is, a search by
means of the most prominent lexical items of the
main chain of the input document. This is described
Figure 3: Lexical meta-chains of an input document
(translated from German).
3.3 Ontology Alignment/Topic
The third module of our applications is concerned
with topic labelling and the categorization of a
document. On base of the resultant meta-chains,
representing the main document content, we are able
to compute a topic label for each section of the input
document. The first step in doing this is to determine
the distribution of tags by employing again a lexical
chaining limited to the entries of the meta-chain and
ranking afterwards each returned keyword to its
IDF/RIDF value in conjunction with the entropy of
word frequencies
. As a result we gain a weighted
The IDF/RIDF-Index was computed on the basement of the German
Wikipedia Project.
WEBIST 2008 - International Conference on Web Information Systems and Technologies
list of tags out of which the topmost ranked units are
selected. In order to classify and label a meta-chain
we are going to align this information to the input
taxonomy. In this case we are using again the social
ontology of the Wikipedia Category System as a
resource (See Gleim, Mehler, 2006). Therefore we
explore the most probable categories and articles of
the Wikipedia categorizing and relating to the input
document. This is done by ‘firing’ search queries on
the calculated index of the article-section of Wiki-
pedia using the weighted tag list. The retrieved arti-
cle weight is computed by frequency of tag occur-
rence within an article. This can be computed by the
following algorithm:
nwt: number of weighted tags
rd: retrieved article
rdw: retrieved article weight
ua: used article
uc: used category
while (rdw < 80%)
submit search query with nwt(tags);
add rd to ua
foreach(item of ua)
parse article-site;
retrieve category in site;
add category to uc
foreach (item of uc)
retrieve hypernym-category in cate-
add new category to uc;
The explored categories are then used as topic-
labels. As an outcome, three different weighted lists
of tags are generated. Firstly, a content-list compris-
ing the ‘classical’ content tags labelled with the
category concepts. Secondly, a category-list as a
subset of Wikipedia categories, tagging the input
document. Thirdly, a hyperlink-list indicating the
most likely connected Wikipedia articles
As a visual depiction all three weighted list are dis-
played as tag-clouds (
Figure 4).
In summary, our system of topic labelling comprises
classical text mining technologies which already
have shown to produce reliable mining results with
rising Web 2.0 technologies. Thus, a central
Figure 4: Tag Cloud-Representation.
outcome of the paper is to show a way to integrate
text & web mining with social tagging systems that
altogether provide semantic search as a future ser-
vice of digital libraries. This paper presented the
architecture of such integration. The evaluation of
the usefulness of its ingredients has already been
provided in the related literature. What remains to be
done is a profound user study which shows the use-
fulness of our system from the point of user commu-
nities of digital libraries. Future work will focus on
systematically evaluating this application, by using a
handcrafted tagged and categorized corpus of the
German newspaper Die Zeit. The web-application is
online accessible at:
Allan J., 2002. Topic Detection and Tracking.
Event-based Information Organization. Kluwer, Bos-
Barr M., Wells C., 1990. Category Theory for Computing
Science. Prentice Hall, New York/London/ Toronto.
Barzilay R., Elhadad M., 1997. Using lexical chains for
text summarization. In Proceedings of the Intel-ligent
Scalable Text Summarization Workshop (ISTS'97),
ACL, Madrid, Spain.
Braun S., Schmidt A., Zacharias V., 2007. SO-BOLEO:
vom kollaborativen Tagging zur leichtge-wichtigen
Ontologie. In Mensch&Computer 2007
Budanitsky A., Hirst G., 2006. Evaluating Word-Net-
based measures of semantic distance. Computational
Linguistics, 32(1):13-47.
Fellbaum C., editor., 1998. WordNet: An Elec-tronic
Lexical Database. MIT Press, Cambridge.
TOWARDS AUTOMATIC CONTENT TAGGING - Enhanced Web Services In Digital Libraries using Lexical Chaining
Gleim R., Mehler A., Dehmer M., Pustylnikov O., 2007.
Aisles through the category forest. In Pro-ceedings
Webist 2007.
Golder S., Huberman B. (2006). Usage patterns of
collaborative tagging systems. In Journal of Infor-
mation Science, pages: 198—208.
Heyer, G., Bordag, S., Quasthoff, U., 2003. Small worlds
of concepts and other principles of semantic search, In
Innovative Internet Community Systems, Proceedings
of the Third International Workshop IICS 2003, June
2003 Leipzig, Lecture Notes in Computer Science,
Springer Verlag: Berlin, Heidelberg, New York
Hirst G., St-Onge D., 1997. Lexical Chains as rep-
resentation of context for the detection and correc-tion
malapropisms. In C. Fellbaum, editor, Word-Net: An
electronic lexical database and some of its applica-
tions. Cambrige, MA: The MIT Press.
Idea N., Pries-Dorman G., 1998. Corpus Encoding Stan-
dard. NewYork. URL:
Leuf, B., Cunningham W., 2001. The Wiki way: quick
collaborationon the Web. In Addison-Wesley.
Lezius, W., 2000. Morphy - German Morphology, Part-of-
Speech Tagging and Applications. In Ulrich Heid;
Stefan Evert; Egbert Lehmann and Christian Rohrer,
editors, Proceedings of the 9th EURALEX Interna-
tional Congress pp. 619-623 Stuttgart, Germany
Lossau N. (2004). Search Engine Technology and Digital
Libraries, Libraries Need to Discover the Academic
Internet. In: D-Lib Magazine, Bd. 10, Nr. 6, ISSN
Mayr, W. (2005). Google Scholar - wie tief gräbt diese
Suchmaschine? Bonn. URL:
Mika P., 2005. Ontologies are us: A unified model of
social networks and semantics. In: Proceedings of the
Fourth International Semantic Web Con-
ference(ISWC2005), Lecture Notes in Computer
Science no. 3729, page 122-136, Galway, Ireland
Morris J., Hirst G., 1991. Lexical cohesion com-puted by
thesaural relations as an indicator of the structure of
text. Computational Linquistics.
O'Reilly, T., 2005: What Is Web 2.0. O'Reilly Media.
09/30/ what-is-web-20.html
Power, R., Scott, D., Bouayad-Agha N., 2003. Document
structure. In: Computational Linguistics, 29(2), 211-260
Silber H.G., McCoy K.F., 2002. Efficiently com-puted
lexical chains as an intermediate representa-tion for
automatic text summarization. Computa-tional Lin-
Voss J., 2006. Collaborative thesaurus
tagging the Wikipedia way. URL: http://
WEBIST 2008 - International Conference on Web Information Systems and Technologies