=Paper=
{{Paper
|id=Vol-464/paper-2
|storemode=property
|title=Information Extraction in Semantic Wikis
|pdfUrl=https://ceur-ws.org/Vol-464/paper-08.pdf
|volume=Vol-464
|dblpUrl=https://dblp.org/rec/conf/semwiki/SmrzS09
}}
==Information Extraction in Semantic Wikis==
https://ceur-ws.org/Vol-464/paper-08.pdf
Information Extraction in Semantic Wikis
Pavel Smrž and Marek Schmidt
Faculty of Information Technology
Brno University of Technology
Božetěchova 2, 612 66 Brno, Czech Republic
E-mail: {smrz,ischmidt}@fit.vutbr.cz
Abstract. This paper deals with information extraction technologies
supporting semantic annotation and logical organization of textual con-
tent in semantic wikis. We describe our work in the context of the KiWi
project which aims at developing a new knowledge management system
motivated by the wiki way of collaborative content creation that is en-
hanced by the semantic web technology. The specific characteristics of
semantic wikis as advanced community knowledge-sharing platforms are
discussed from the perspective of the functionality providing automatic
suggestions of semantic tags. We focus on the innovative aspects of the
implemented methods. The interfaces of the user-interaction tools as well
as the back-end web services are also tackled. We conclude that though
there are many challenges related to the integration of information ex-
traction into semantic wikis, this fusion brings valuable results.
1 Introduction
A frequently mentioned shortcoming of wikis used in the context of knowledge
management is the inconsistency of information that often appears when wikis
are put to everyday use of more than a few knowledge workers. Semantic wikis,
combining the easy-to-participate nature of wikis with semantic annotations,
have a strong potential to help in this situation and to become the ultimate
collaborative knowledge management system. However, adding metadata means
additional work and requires user’s attention and thinking. Since it is often
difficult to give users immediate satisfaction in reward for this tedious work, an-
notations in internal wikis tend to be rather scarce. This situation has a negative
impact on comprehension of the advantages of semantic wikis and discourages
their extensive deployment.
Information extraction (IE) can be seen as a means of reducing user’s anno-
tation workload. It refers to the automatic extraction of structured information
such as entities, relationships between entities, and attributes describing entities
from unstructured sources [19]. The state-of-the-art IE technology can produce
metadata from content, provide users with useful suggestions on potential an-
notations and ask questions relevant for the current context. The ultimate goal
of IE in semantic wikis is to maximize the benefits of the rich annotation and,
at the same time, minimize the necessity of manual tagging.
2 Pavel Smrž and Marek Schmidt
New application domains raise various challenges for large-scale deployments
of IE models. Despite more than two decades of intensive research, the accu-
racy of the systems is still unsatisfactory for many tasks. Moreover, the results
strongly depend on the domain of applications and the solutions are not easy to
be ported to other domains. Language dependency is also an issue as the level of
analysis required by some methods is available only for a few languages. Another
difficulty, particularly significant for the use of IE technology in semantic wikis,
lies in the limited character of examples that could be used to train extraction
models. Indeed, the real use of semantic technologies calls for specialized an-
notations of complex relations rather than simple and frequent entities such as
places, dates etc. Users are not willing to look for more than one or two other
occurrences of a particular relation that should be automatically tagged.
The issues related to standard IE solutions also determine the work described
in this paper. As almost all realistic IE-integration scenarios involve system sug-
gestions and user interaction, the IE components that have been designed and
are successively developed can be taken as a kind of semantic wiki recommen-
dation system. We pay a special attention to the “cold-start problem” which
appears in the beginning of the technology deployment when there are no data
to provide high quality suggestions. Taking the semantic wiki as an open link-
ing data platform (rather than a tool to enrich data with semantics for internal
purposes only) helps in this respect as one can immediately take advantage of
external data sources. We also deal with the incremental character of IE tasks
running on gradually growing wiki pages. The implemented interfaces of the IE
services facilitate the process of continuous updating of the annotations. They
also help to interlink external resources that are modified independently of the
controlled updates in the internal wiki.
The following sections tackle the mentioned challenges and show how IE can
be used in real semantic wiki systems. As a use case, the next section briefly
introduces the IE techniques and tasks applied in the KiWi project. Section 3
discusses specific features of the IE techniques required by the semantic wikis and
the innovative aspects of the KiWi solutions. We conclude with future directions
of our work.
2 IE Techniques and Tasks in the KiWi project
2.1 Conceptual Model
The main objective of the KiWi (Knowledge in a Wiki1 ) project is to facilitate
knowledge sharing and collaboration among the users of the system to manage
knowledge in a more efficient way [20]. Together with personalization, reasoning
and reason maintenance, IE belongs to the key enabling technologies in KiWi.
There are two main use cases in this project. The first one is provided by
Sun Microsystems, Prague, and is focused on knowledge management in software
development, particularly in the NetBeans project. The second one addresses
1
http://www.kiwi-project.eu
Information Extraction in Semantic Wikis 3
vital issues in process management in Logica. The examples given in this paper
are taken from those use cases.
KiWi allows users to add meta-data to individual pages or their parts in the
form of free or semantic tags. The role of IE is to support users in creating the
semantic meta-data and making the knowledge explicit so that it can be further
queried and reasoned in a semantic way. The conceptual model for IE in KiWi
consists of three major components:
– content items;
– text fragments;
– annotations of content items.
Content item refers to any entity that can be identified. Text fragment is an
arbitrary continuous piece of a content item. Text fragments are content items
themselves. It enables adding metadata to individual text fragments. In a simple
case of commenting a piece of information on a wiki page, the metadata can be
of type “comment” and can contain the text of the comment. Tagging text
fragments provides a bridge between structured and unstructured information.
The fragments can be taken as generalizations of links representing any kind of
related resources. In that sense, the fragments are independent of the formatting
structure of the wiki page.
Figure 1 demonstrates the way in which information extracted from three
different content items (wiki pages) is put together. All the pages mention the
same resource – an issue identified by its number #1223 (corresponding to Sun’s
bug-reporting site IssueZilla). In the “Description of Issue #2536”, one can read
that the issue is actually a consequence of Issue #1223. The page on “Release
Engineering” page says that issue #1223 is critical for the current release of the
final product. Finally, “Meeting minutes” assign the task to fix the bug to Joe.
The information extraction component is able to extract the mentioned pieces
of information and save them (as a set of RDF triples) for further processing.
Meeting Minutes
Joe will fix #1223
assignedTo(Joe)
Issue #1223
Issue #2556 Release Engineering
is caused by #1223 #1223 is a RC bug
causes(#2556) releaseCritical
Fig. 1. Information about one entity may come from different pages
Note that the described concept of tagging text fragments that represent a
resource (rather than to simply join the ascertained statement to the particu-
4 Pavel Smrž and Marek Schmidt
lar resource) enables identifying sources of extracted information, synchronizing
text and tags, keeping track of the consequences of the changes and pointing out
inconsistencies. What is even more important for the semi-automatic IE pro-
cesses, it also makes the manual corrections easier and allows users to improve
the IE accuracy by providing explicit feed-back to the system’s decisions.
KiWi is designed as a modular system, in which modules provide additional
functionality to the core system via widgets which a user may add to her custom
layout. The main interaction between the IE system and the user is realized by
the annotation widget. Figure 2 demonstrates the use of the widget for IE from
meeting minutes. It shows information extracted from the text fragment appear-
ing on the wiki page. The widget also offers actions enabled by the semantics
of extracted information (such as inserting the event to the calendar, showing
the map of the place, etc.). The annotation editor that has been also developed
allows users to manually annotate fragments directly in the KiWi editor and to
inspect associated semantic information in the annotation widget.
Content Editor Annotation Widget
... Business Trip (event)
Alice will come to Salzburg on May 21st Alice Smith (employee)
Salzburg (place)
03/21 (date)
...
Accept Reject
Add to Calendar
Show Map
Fig. 2. An example of metadata and action suggestions provided by the KiWi anno-
tation component
KiWi aims at an application of the state-of-the-art IE methods in the se-
mantic wikis [8]. Various techniques and methods are employed to fulfil the key
tasks identified in the project.
IE from wiki pages deals mostly with free text. In general, it can therefore
benefit from a thorough language analysis of the input. In addition to tokeniza-
tion and sentence splitting, the natural language processing may involve stop-list
filtering, stemming or lemmatization, POS tagging, chunking and shallow or deep
parsing. Many of these pre-processing steps are computationally expensive. Al-
most all of them are strongly language-dependent. Moreover, there is danger of
cascading of errors from pre-processing in the IE system. That is why we follow
the current trend and apply selective pre-processing in KiWi. The methods de-
scribed below take into account the context in which they work and employ only
those pre-processing processes that can bring significant value to the IE itself.
Information Extraction in Semantic Wikis 5
2.2 Implemented IE Techniques
The IE solutions implemented in KiWi rely on several state-of-the-art IE tech-
niques. Before discussing the function of particular IE processes in KiWi, let us
therefore mention the crucial techniques employed. The following technologies
play a key role in the system:
– automatic term recognition combining domain-specific and general knowl-
edge;
– computation of word relatedness to define similarity measures;
– text classification and sense disambiguation based on advanced machine-
learning methods;
– dimensionality reduction for text feature vectors.
Any realistic approach to automatic term recognition (ATR) from wiki pages
cannot ignore the fact that the source texts are usually rather short. Unfortu-
nately, most of available ATR methods rely too much on high frequency counts
of term occurrences and, therefore, cannot be utilized in the intended field.
To cope with the problem, we adopt a new ATR method proved to give
the best r results suggestesults in our previous experiments (see [9]). It flexi-
bly combines the frequency-based measure (a variant of the TF.IDF score) and
the comparisons with a background corpus. The current implementation works
with a general background data (such as American GigaWord [5] or Google
TeraCorpus [1] for English) only. Our future work will focus on an automatic
identification of supplementary in-domain texts that would be useful for the
“focused background subtraction”.
Various approaches to characterize the semantic distance between terms have
been also explored in our research. For general terms, we make use of the
wordnet-based similarity measures [16] that take into account the hierarchical
structure of the resource. The same technique is employed when the closeness of
concepts in a domain-specific thesaurus or ontology is to be computed (e.g., on
Sun’s Swordfish ontology [3]).
An implemented alternative method which does not require manually cre-
ated resources (such as wordnet-like lexical databases or domain ontologies)
determines the semantic similarity of terms by the relative frequency of their
appearance in similar contexts. Of course, there are many ways to assess the
similarity of contexts. The results of our preliminary experiments suggest that
the best performer for the general case is the method taking into account the
(dependency) syntactical structure of the contexts [7, 10] (terms are semanti-
cally close if they often appear in the same positions, e.g., as subjects of the
same verb, modifiers of the same noun, etc.).
Many IE tasks can be formulated as classification problems. This finding is
behind the immense popularity of machine learning techniques in the IE field
today. In the rare case when there are enough data for training, KiWi follows
this trend. Complex features computed on the dependency structures from the
source text are gathered first.
6 Pavel Smrž and Marek Schmidt
The particular set of features applied depends on the task and the language
in hand. For English named entity recognition, a gazetteer, word contexts, lexi-
cal and part of speech tags are used. For classification of the role an entity plays
on a page (which can be interpreted as a semantic role labeling problem [13]),
additional features provided by a dependency parser are employed. The classi-
fication is performed by CRF (Conditional Random Fields) and SVM (Support
Vector Machine) models with tree kernels constructed from syntax trees of the
sentences [21]. Depending on the context, the process can identify “soft cate-
gories” sorted by the descending probability of correctness. The resulting N-best
options are presented to the user who chooses the correct one.
As opposed to the discussed situation, a typical classification task in the
context of semantic wikis can be characterized by the limited character of the
input text and the lack of data to train the classifier. The advanced methods that
can deal with the latter issue are discussed in the next section. Let us therefore
just note, that to overcome the former one (inherent to the wiki world), KiWi
harnesses the other mentioned techniques and personal/organizational contexts
to characterize the “ground” of the material provided by the user and to increase
accuracy of the classification.
As exemplified by the Logica use-case in KiWi, the semantic wikis in the
professional setting often need to integrate large sets of business documents
(product specifications, customer requirements, etc.). Having such a document
in hand, the user can ask the system to find similar documents in the given
collection. As the terminology and the style of the documents can differ signif-
icantly, the straightforward computing of the similarity as a function of term
co-occurrences is often insufficient. Standard approaches to overcome this (such
as PLSA – Probabilistic Latent Semantic Analysis or LDA – Latent Dirichlet
Allocation) transform the term vectors representing the documents to point out
their semantic closeness.
Unfortunately, the computation of such transformations is prohibitively ex-
pensive. KiWi draws on the random indexing technique [6] that is several orders
of magnitude faster than the mentioned approaches. As KiWi documents are
indexed by means of Apache Lucene search library – we take advantage of Se-
mantic Vectors [22] – a fast implementation of the random indexing concept
based on the Lucene indices. This setting provides very efficient mechanism to
evaluate similarity queries in KiWi.
2.3 IE Tasks in KiWi
The above-mentioned IE techniques find their application in various tasks and
various contexts in the KiWi system. From a general point of view, the whole
IE functionality can be seen as tag suggestion or automatic annotation (if the
similarity is interpreted as a special kind of tagging). On the other hand, the
user perspective distinguishes different kinds of tags for different purposes. The
following tasks form the core of the KiWi IE module in the latter sense:
– suggestion of new free-text tags and thesaurus/ontology extensions;
Information Extraction in Semantic Wikis 7
– entity recognition and semi-automatic annotation of content items;
– relation extraction and structured tag suggestion;
– similarity search adapted according to the user’s feedback.
Figure 3 (based on the KiWi use case defined by Logica) demonstrates the
interplay of these tasks. It shows a situation when a project manager comes to
the task to produce a project risk analysis report based on her notes from a
preparatory meeting (as displayed in the KiWi annotation editor on the left side
of the picture). Risk-related information needs to be formalized, the potential
impact should be identified and the resolution strategies explicitly stated. Based
on the user-specific setting, the IE component automatically identifies entities
such as company products, development technologies, names of employees, dates,
places, etc. and classifies the page as a (seed of) risk analysis report – a known
type of document with an associated semantic form. The identified type narrows
down the similarity search which focuses on those risk analysis reports that men-
tion semantically related risks (it is realized as a simple word-based relatedness
function on the “identified risks” sections in the current implementation).
Fig. 3. KiWi annotation component classifying entities and relations and suggesting
tags and projects related to a given page according to the identified risks
The annotation component also suggests terms found in the text as additional
tags. The possibility to propose free-text tags is not particularly useful in the
semantically-rich case discussed but it can be essential for “lightweight” semantic
wiki environments. A more practical function in the actual context refers to semi-
automatic extending the conceptual domain model. The most frequent form of
this process regards thesaurus or ontology population by instances referred to in
the analysed text. For example, finding new term “JBoss Seam” in the position
where a development tool name is expected, the system can suggest adding the
8 Pavel Smrž and Marek Schmidt
term as an instance of class “development tools”. The problem domain ontology
can also be extended in higher levels, e.g., “video meeting room” can be suggested
as a subclass of “meeting room”.
Entity recognition employs the FSA (finite-state automaton) technology and
implements a straightforward gazetteer strategy when it is tightly coupled with
the annotation editor to identify types and instances of entities mentioned in the
text and to suggest annotations linking the specific reference to the knowledge
base. A limited set of rules is applied to identify compound expressions such as
names, absolute temporal terms, monetary and other numeric expressions, etc.
Apart from that, the functionality is completely based on lists of entities that
should be identified in the texts. The lists are populated by terms referring to
concepts in general ontologies (e.g., UMBEL2 or GeoNames3 ) as well as domain-
specific resources (such as Sun’s Swordfish ontology or a list of team members
and their roles). For the fast on-line processing, these extensive lists are compiled
to a large FSA which is then used to identify matches in the text and to provide
the type of the suggested tag.
Similarity search takes advantage of pre-computed matrices of term relat-
edness. This is crucial especially for comparing short text fragments such as
the “identified risk” sections discussed above. Particular matrices correspond to
various measures of the semantic distance between terms. Except for the batch
document clustering, the similarity search is always intended for the on-line
mode. The pre-computation of the term similarities in the form of the matrices
helps to speed up the search significantly.
For the fast search on short text fragments (less than a paragraph), KiWi
computes the information gain of the terms appearing in the text. The lines
corresponding to the most informative terms are taken from the term-closeness
matrices. This provides a simple query-expansion mechanism. The query com-
bining the terms from the actual fragment and the semantically close terms
(weighted by the informativeness and similarity, respectively) is evaluated on
the all content items of the same type and the best matches are retrieved.
The whole wiki pages and full documents are indexed by the Lucene library.
To perform similarity search on this kind of documents, SemanticVectors [22]
are employed. It is often the case that the retrieved documents do not come up
to user’s expectations. The most informative terms can prove to be unimpor-
tant from the user’s perspective. That is why it is very important to let KiWi
users know why the particular documents are considered similar to that one
in question and what terms played the key role in the system’s decision. KiWi
lists those terms for the entire set of the similar documents and for each indi-
vidual document as well. The user can mark some of the terms as unimportant
for the current context and the system re-computes the similarity with the new
restricted set of terms.
The concept of tags in KiWi is rather general. It comprises the standard
label-like tags, but also structured ones that encode relations of the concept
2
http://www.umbel.org
3
http://www.geonames.org
Information Extraction in Semantic Wikis 9
represented by the given term to other concepts. The corresponding IE task
of relation extraction extracts facts from relations between entities in a wiki
page (e.g., from statements like Alice will travel to Salzburg on May 21st). The
relation can also be identified between an entity and the wiki page itself, since
every page in the KiWi represents some entity.
The implementation of the relation extraction algorithm is similar to that
of entity recognition. It employs advanced machine learning models (CRF men-
tioned above) and incorporates additional information provided by the user to
improve the performance. For example, the user can specify features relevant
for semi-structured documents as an XPath expression (e.g., to inform the auto-
matic extraction method that the cost is always in the second column of a table).
Unfortunately, the process is prone to the errors in the language analysis layer
so that the results strongly depend on the quality of the language-dependent
pre-processing phase.
Semantic wikis with annotations support evolution of knowledge from free-
form to structured formalized knowledge. The role of IE is to support the user
in the process of creating semantic annotations. If the structure of knowledge is
well understood, the annotations can take a unified form of tags anchored in a
domain ontology. However, the “wiki way” of knowledge structure that is only
emerging in the collaborative work process calls for sharing of free-text tags as
well. KiWi supports this by means of new tag suggestions based on the ATR
(see above) from a particular document or a wiki page. Users can choose which
extracted terms are appropriate to tag the resource and what their relations to
other tags are. For ATR on short wiki pages, KiWi engages heuristics based on
simple grammar patterns (such as “an adjective followed by a noun”) to propose
the candidate terms.
In addition to free-text tagging, ATR makes it also possible to suggest ex-
tensions to a domain ontology or thesaurus. KiWi checks whether the extracted
terms correspond to existing concepts and if not, it proposes additions. If there
are enough data for classification training, it can also find the most probable
class to link the new concept to.
3 Innovative Aspects of IE in KiWi
As mentioned above, there are many challenges and open questions related to
the use of IE in semantic wikis. The state-of-the-art IE systems [2, 12, 17] often
make assumptions about the type of data, its size and availability, and the user
interaction mode that are not acceptable in the given context. KiWi explores
solutions that are able to cope with the problems and work the “wiki way”
(provide sophisticated functionality but easy to understand and easy to use).
Machine learning plays a central role in the current IE paradigm [14]. From
a conceptual point of view, statistical IE systems distinguish two phases: the
training phase and the deployment phase. In the training phase the system ac-
quires a model that covers a given set of annotation examples. In the deployment
phase, the system identifies and classifies relevant semantic information in new
10 Pavel Smrž and Marek Schmidt
texts, i.e., texts that were not included in the training set. The predominant ap-
proach expects a large text corpus with annotated information to be extracted,
and then uses a learning procedure to extract some characteristics from the an-
notated texts [23]. Unfortunately, an annotated training data set is available for
a very limited number of cases. And it is unrealistic to expect that KiWi users
will provide this kind of data to make the system “semantics-aware”. This is
especially true for the case of many application-specific relations in the explored
domains.
To overcome the problem of training data scarcity, IE in KiWi explores a
combination of the standard supervised algorithms with the methods that are
able to learn from untagged texts. We take advantage of the concept of boot-
strapping, which refers to the technique that starts from a small initial effort
and gradually grows into something larger and more significant [14]. One of the
currently employed methods relying on this principle is the expansion. An ini-
tial extraction model (learned from few examples) is applied to the unannotated
text (wiki pages, linked documents or external resources) first. Newly discovered
entities or relations that are considered sufficiently similar to other members of
the training set are added and the process is iterated.
Another approach we apply is active learning. In active learning, the system
itself decides what the best candidates for annotation are in order to maximize
the speed of the learning process. A user is then asked to annotate these instances
only. The idea of active learning perfectly fits the wiki philosophy that every
user can annotate every page for which she has sufficient rights. All changes are
naturally reported and there is no problem to come back to a previous version
in case somebody made inappropriate annotations.
The combination of both methods lets the system exploit the knowledge as
much as possible, but still allows users to have full control of the annotation
process.
There is not much to do about the dependency of the IE methods on the
result of the pre-processing phase. The trade-off between the quality of the lan-
guage analysis and the general availability of the corresponding tools makes it
impossible to provide the same grade of extraction in all languages. KiWi tries
to mitigate the “curse of language-dependency” by means of using general re-
sources that are available across languages. For example, our experiments with
instances of Wikipedia in several languages used for the expansion proved that
this functionality does not need to be limited to a particular language.
In addition to the lack of annotated data for training classifiers, there is also
a specific problem of the unusual nature of some IE tasks in semantic wikis.
The resources that are to be semantically annotated vary exhibit high diversity.
The length ranges from a few words to entire pages and full documents that are
uploaded to the system. Especially the lower side on this scale (very short texts)
trouble the commonly used IE techniques – they often need more material to
find similar contexts, to disambiguate a term, to classify a relation, etc.
One of the techniques that partially deals with the problem of short texts
benefits from the PLSA and random projection algorithms discussed above. It
Information Extraction in Semantic Wikis 11
projects the dimensions given by the original set of terms to the space defined by
a referential collection of resources. In the case of KiWi, pages from Wikipedia
are taken as the dimensions. Thus, it is possible to present the results to the
user in an intuitive form – pointing out the articles with the most significant
contribution.
The concept of KiWi as the open linking data platform has been already
mentioned. The IE technology tries to re-use as much as possible from exist-
ing semantic web knowledge resources. Dbpedia and Wikipedia find their place
in the training of classifiers and sense disambiguators, the taxonomy based on
WordNet and OpenCyc help to define the similarity measures etc. The external
data sources are also linked to the user-interaction mode in KiWi. For example, a
user defines new semantic tag “programming language” as http://umbel.org/
umbel/semset/en/wikipedia/Programming\_language. The system fetches all
relevant articles from Freebase and trains an initial classifier. The user can start
to tag with it immediately and to provide feedback to improve the model.
4 Conclusions and Future Directions
Let us summarize the major point of the work reported in this paper. The
application of IE methods on the specific set of problems (texts of varying size
and character, complex relations, etc.) with this kind of user interface (semi-
automatic, generic, ontology based) is novel. In addition to other results, KiWi
brings valuable insights into the practical applicability of the best IE techniques
in real conditions.
KiWi promises an advanced knowledge management system with state-of-
the-art personalization, reasoning and information extraction features. As the
project is still in its early phase (the first year finished in February 2009), only
the first open source pre-release of the core system is available for real use. The
IE components applicable in the context of the mentioned use-cases have been
developed in parallel and are available in the experimental mode.
The accuracy of the IE methods significantly depends on the domain, task
and data that can be used. For example, the reported figures for entity recog-
nition range from 60 % to 90 % and generally correspond to the type of entities
to be extracted [15]. The precision of the relation extraction task demonstrates
even more significant variability (e.g., [18] reports results ranging from 7 % to
90 % on various relations from Wikipedia). It has been shown that the IE process
can be useful even if the performance is imperfect [4]. However, to the best of
our knowledge, no studies assessed the actual added value of the IE solutions
for the highly interactive scenarios which is typical for the semantic wikis. This
forms one of the key directions of our future work.
Another challenge we have to face in the next stage comes from the fact
that the types of entities and relations to extract are not specified in advance.
Users can apply the services to extract information from arbitrary complex texts.
They can also specify an ontology and ask the system to identify any given
relation. While, e.g., the use of ontologies to drive the IE process has been
12 Pavel Smrž and Marek Schmidt
already explored [11], it is not yet clear whether the performance of the general
IE system, capable of extracting any type of entity or relation only by learning
from the user annotations, will be acceptable for the end-users.
Acknowledgement
The work presented in this paper has been supported by European Commis-
sion, under the ICT program, contract No. 211932 and under the IST program,
contract No. 27490.
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