=Paper=
{{Paper
|id=Vol-293/paper-5
|storemode=property
|title=Enabling the Semantic Web with Ready-to-Use Web Widgets
|pdfUrl=https://ceur-ws.org/Vol-293/paper5.pdf
|volume=Vol-293
|authors=Eetu Makela,Kim Viljanen,Olli Alm,Jouni Tuominen,Onni Valkeapaa,Tomi Kauppinen,Jussi Kurki,Reetta Sinkkila,Teppo Kansala,Robin Lindroos,Osma Suominen,Tuukka Ruotsalo,and Eero Hyvnent,pages 56-69
|dblpUrl=https://dblp.org/rec/conf/semweb/MakelaVATVKKSKLSRH07
}}
==Enabling the Semantic Web with Ready-to-Use Web Widgets==
https://ceur-ws.org/Vol-293/paper5.pdf
FIRST - First Industrial Results of Semantic Technologies
Enabling the Semantic Web with Ready-to-Use
Web Widgets
Eetu Mäkelä, Kim Viljanen, Olli Alm, Jouni Tuominen, Onni Valkeapää, Tomi
Kauppinen, Jussi Kurki, Reetta Sinkkilä, Teppo Känsälä, Robin Lindroos,
Osma Suominen, Tuukka Ruotsalo, and Eero Hyvönen
Helsinki University of Technology (TKK) and University of Helsinki
first.last@tkk.fi, http://www.seco.tkk.fi
Abstract. A lot of functionality is needed when an application, such
as a museum cataloguing system, is extended with semantic capabilities,
for example ontological indexing functionality or multi-facet search. To
avoid duplicate work and to enable easy and cost-efficient integration of
information systems with the Semantic Web, we propose a web widget
approach. Here, data sources are combined with functionality into ready-
to-use software components that allow adding semantic functionality to
systems with just a few lines of code. As a proof of the concept, we present
a collection of general semantic web widgets and case applications that
use them, such as the ontology server ONKI, the annotation editor SAHA
and the culture portal CultureSampo.
1 Introduction
To implement new semantic applications or to extend existing information sys-
tems (e.g. a museum cataloguing system) with semantic capabilities requires a
lot of functionality dealing specifically with ontologies and metadata. Currently,
needed functionalities are typically created for each application individually, re-
quiring a lot of work, time and specific skills. Being able to lower these implemen-
tation costs would be hugely beneficial to the adoption of the Semantic Web [1]
as a whole. On a general level, there are three tasks in any semantic information
environment that need to be handled, either by humans or machines:
Semantic Content Consumption. Searching, browsing, visualizing and oth-
erwise consuming semantic content. In this group belong for example library
users in a semantic library environment and visitors of a semantic museum
portal [2]. RSS aggregation services are a programmatic example.
Content Indexing. The production of semantic metadata by indexing and
publishing content with references to shared vocabularies (e.g. museum cu-
rators indexing exhibits). Sometimes, the end-users themselves fill the role
of content indexers, as in the social bookmarking site del.icio.us1 and photo
sharing site Flickr2 .
1
http://del.icio.us/
2
http://flickr.com/
56
FIRST - First Industrial Results of Semantic Technologies
Ontology Maintenance and Publishing. Creating and maintaining the on-
tologies used as references for both semantic indexing and retrieval. In orga-
nized fields, this is often done by dedicated information workers, but again
in the case of Web 2.0 [3] sites, it may be the users themselves that develop
their vocabulary in an ad-hoc manner alongside indexing.
We argue that in many cases, tasks in the contexts above contain many com-
mon general subtasks. Specifically, we have found at least the following common
functionalities: 1) concept and instance selection, 2) semantic linking, 3) concept
and instance viewing, and 4) shared concept and instance storage and mainte-
nance. To avoid duplicate work and to enable more individuals and organizations
to join the Semantic Web, we propose a web widget 3 approach, where these func-
tionalities are created and published as ready-to-use software components which
can easily and cost-effectively be added to applications.
A web widget is a reusable, compact software component that can be em-
bedded into a web page or application to provide functionality. Most useful web
widgets also combine on-line data resources with site data to create mash-ups,
such as in usual use cases of the Google Maps and Google AdSense web widgets.
Web widgets can also be combined together and published as new components,
e.g. with the Yahoo! Pipes service4 . What makes web widgets very interest-
ing, is that they allow developers to easily add otherwise very complicated or
costly features to virtually any application. For example, Google Maps provides
a map and satellite image database of the planet Earth combined with search
and browsing capabilities for all to use.
Semantic web widgets then, as we envision them, are software components
that: 1) are hooked up with either Semantic Web data sources such as ontologies
or instance databases, or the processed outputs of other components, 2) offer
a single, compact functionality, yet do that as completely as possible (often
including user interface elements), 3) are amenable to be combined with other
components and data to solve complex problems, and 4) can be easily and cost-
efficiently used for adding semantic functionalities to an application.
During our research, we found that while the semantic functionalities we
want to implement themselves are general, the best implementation for them
varies with the type of the underlying data they are to be hooked up with.
For example, selection from a geographical location ontology naturally benefits
from map-based user interfaces, actor ontologies need handling of pen names
and transliterations [4], while concept ontologies such as the Suggested Upper
Merged Ontology SUMO [5] and the health ontology SNOMED CT5 require
other methods. Therefore, we present several widget solutions to each of the
tasks based on the different requirements of each content domain.
The context for this work is the goal of creating a national semantic web
infrastructure in Finland [6], where critical Semantic Web resources, such as
3
http://en.wikipedia.org/wiki/Web widget
4
http://pipes.yahoo.com/
5
http://www.snomed.org/snomedct/
57
FIRST - First Industrial Results of Semantic Technologies
ontologies and the web widgets for using them, are published as centralized
services.
In the following, as proof of the viability of the idea of semantic web widgets,
we first present some of the components we have created for solving common
subtasks, and then apply them to one combined mash-up service, the ONKI
ontology server, and two end-user applications: the SAHA metadata editor for
content creation and the CultureSampo cultural heritage portal. Finally, related
work is presented, followed by discussion and suggestions for future work.
2 Common Subtasks in Semantic Applications and
Widgets for Solving Them
2.1 Concept and Instance Selection
In ontological systems finding and selecting the right concepts and instances is a
central task of its own in ontological user interfaces. For end-user applications,
any search usually begins by first finding the right concepts with which to do the
actual ontological querying [7]. For efficient semantic content indexing, accurate
indexing entitities need to be found with as little effort as possible [8]. Also
ontology developers need concept search when creating links between concepts,
especially when developing distinct, yet heavily interlinked ontologies. In the
following, web widgets to provide efficient concept selection in different situations
are presented.
Semantic Autocompletion and Context Visualization When the user
knows with relative certainty what they are looking for, and the labels of the
entities do not overlap much, as in most non-instance vocabularies, a keyword
search is a natural way of selecting concepts. For this, we have developed multiple
text literal matching semantic autocompletion interfaces [9] that can be hooked
into various semantic data sources to provide concept selection functionality.
Particular among these are two that also expose the semantic contexts of the
concepts matched.
There are two main reasons for desiring such functionality. First, this allows
the user to get acquainted with the vocabularies and how they are organized.
Second, particularly with large complex interlinked vocabularies, it is never guar-
anteed that the concept that first occurs to the user is the best one for their task.
Showing the ontological context or otherwise derived concept recommendations
is a powerful way of gently guiding the user and giving more options.
The first of the context exposing interfaces created, depicted in figure 1(a)
shows the autocompletions directly inside a tree. This is applicable when the en-
tities form a hierarchy and this is clearly the most important context for them. A
particular case for this is in view-based search, where the view tree is usually al-
ready shown for visualization and selection purposes. The second is a navigation
widget for exploring the contexts of matched entities, depicted in figure 1(b).
Here the ontological context and otherwise derived concept recommendations
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FIRST - First Industrial Results of Semantic Technologies
are shown to the user as a tree menu, with new levels of context opening when
the user mouses over the concepts.
(a) inside a tree (b) with context navigation
Fig. 1. Semantic autocompletion
Map-based Search and Visualization URIs concerning geographic objects
might carry coordinate information, e.g. in terms of WGS84 latitude and lon-
gitude. We have created a method, n-point search [10, 11], for selecting entities
based on this kind of coordinate data. A search query in this method is done by
pointing out n points on a map. The user clicks on the map and a search poly-
gon is formed accordingly. If an area point of a certain place is found inside the
user-given polygon, or a region-defining polygon is found to overlay the search
polygon, the region instance is retrieved and added to the results.
We have also taken into account two special cases, namely, where n = 1 or
n = 2. If n = 1 a circle is drawn around the point and the places that are inside
the circle are retrieved. An alternative treatment would be to simply find the
nearest places. If n = 2, we create a bounding box where the points are the
opposite, e.g. South-West and North-East corners, of the search area. We have
implemented the n-point search as a mash-up that itself uses Google Maps6 .
We used SVG [12] for drawing the polygon as a transparent layer on top of the
map. In addition to selecting locations, the component is also able to visualize
semantic content related to geographical locations on the map.
An example of using the component is depicted in figure 2. The n polygon
corners are depicted as small crosses. The system has found from a historical
place ontology [13] the municipality of Viipuri in annexed Karjala as it existed in
1906-1921, because its center points are situated within the user-specified search
polygon. The municipality is visualized on the map as a red circle. The figure
also depicts another useful feature of our component, in that it is able to overlay
multiple maps of our own on top of the ones provided by Google [11]. Here, a
6
http://www.google.com/maps
59
FIRST - First Industrial Results of Semantic Technologies
historical map overlay of the area shows how Viipuri looked at the time specified,
which can be constrasted to how the place looks now.
Fig. 2. Search and visualization using loca- Fig. 3. A floatlet (encircled) displays
tion data and overlaid maps. links to MuseumFinland.
Multi-Facet Search View-based, or multi-facet search is a paradigm that has
recently become prominent as an easy to use interface for querying semantic
content [14–16]. Here, the idea is to offer multiple views to different aspects of
the content, both to visualize it and to select a subset from it by specifying
constraints in the views [7].
We have implemented a general multi-facet search engine that plugged into an
instance database uses the other created visualization and selection components
as views. This also represents one of the more complex interaction patterns
between components, as first the views provide constraint selection for the search
engine, which then calculates a result set based on the instance database, and
feeds this result set back to the views for visualization. As an example, the
selection tree in figure 1(a) is actually part of such a configuration, with the
numbers representing how many items annotated with that concept are in the
current multi-facet search result set. An earlier version of the component [17] has
already been used in the portals MuseumFinland7 [2], Orava8 [18], Veturi9 [19]
and SW-Suomi.fi10 [20]
Concept Location, Disambiguation and Extraction from Text It is of-
ten useful to be able to locate concepts in textual resources. In annotating doc-
uments, suggestions for annotations can be found from the text [21, 22]. In web
7
http://www.museosuomi.fi/
8
http://demo.seco.tkk.fi/orava/
9
http://demo.seco.tkk.fi/veturi/
10
http://demo.seco.tkk.fi/suomifi/
60
FIRST - First Industrial Results of Semantic Technologies
browsing, on the other hand, semantic content can be linked to topics being
discussed in the text [23].
We have implemented a component [8] that can locate concepts and instances
in text documents based on the labels associated with them. The extraction pro-
cess starts with document preprocessing, which in the case of HTML documents
means extracting the textual content from the document. Then, the text is tok-
enized and lemmatized if needed. Next, the extraction component iterates over
the tokenized document and finds strings corresponding to the concept labels. In
cases where labels are ambiguous (e.g. “bank”), the component can make use of
the ontological neighbourhoods of the concepts by counting nearby occurrences
of neighbour concept labels of each candidate in order to guess the proper mean-
ing. After finding the matches, the component then tags the occurences of the
concepts and instances in the document and outputs this tagged copy.
2.2 Semantic Linking
Semantic metadata combined with logical rules makes it possible to automati-
cally link related content together to support semantic browsing (semantic rec-
ommendations) [24, 2]. Automatic linking is an especially important feature in
Semantic Web based systems where the content is typically aggregated from
different sources. Here, manual linking is difficult because the content providers
typically consider only the local view on their content excluding the global view
on the aggregated content [25].
For automatic link generation, we have created functionalities based on three
different techniques. First, used in the already mentioned portals MuseumFin-
land, Orava and SW-Suomi.fi is the rule-based linking server Ontodella [24],
which is accessed by a simple HTTP request containing the URI of the current
document. The system then responds, based on the item metadata and ontolo-
gies linked to it, with a set of related documents. Each recommendation also
contains a human-readable explanation of the relation between the current and
the recommended document. For example, when looking at a nautical flag in
MuseumFinland, the object is linked to sailor’s clothes because, based on the
metadata and the ontologies, they are used in the same situation, seafaring.
Our second link generation component is based on calculating a similarity
measure between items in the linked instance database using an event-based
schema, which allows one to compare items annotated using dissimilar anno-
tation schemas [26]. This is the component used in CultureSampo, the case
application described later in this paper.
Finally, particularly for inter-portal semantic linking, we have exposed the
multi-facet functionalities of our portals to mash-up use. For utilizing them,
we propose the concept of floatlets, semantic linking widgets that can be eas-
ily plugged into any web page. Based on metadata and shared ontologies, the
floatlet is able to make semantically relevant queries to the portals and show
them in the context of the page. For example, figure 3 shows how the Finnish
61
FIRST - First Industrial Results of Semantic Technologies
Broadcasting Company’s video archive11 has been semantically linked based on
metadata with relevant content in MuseumFinland. In the example, the current
video is about the history of speed skating, which has also been described in its
metadata. Based on this information, the floatlet is able to query for old skates
from MuseumFinland. By clicking on the floatlet links, the skates can be exam-
ined in more detail in their original portal. From the semantic portal publisher’s
point of view, floatlets provide a new way for publishing and promoting their
content on the web. By clicking on the floatlet links, new visitors move over to
the floatlet’s host portal.
The idea of floatlets is similar to Google AdSense12 which is used for adding
advertisements to web pages. However, in floatlets the returned links are based
on explicit ontological annotations. This allows the web developer to specify in
detail what information is to be linked, either manually or based on the metadata
of the current web page.
2.3 Concept and Instance Views
To be able to comprehend the meanings of concepts and their relations, content
visualization techniques are needed. The simplest way to approach this is to
show the properties of a concept or an instance to the user, e.g. simply as a list
of properties and their respective values. If a value is a resource, it can also act
as a link to allow browsing the content.
Ontologies are typically organized into hierarchical structures based on sub-
sumption, partition or other properties. This structural context of the concept
gives important information about its meaning and relations to other concepts.
These functions can be fulfilled by such context visualizations as already de-
scribed with reference to semantic autocompletion and depicted in figure 1.
2.4 Shared Concept and Instance Storage and Maintenance
In many semantic applications, there is a need for storing ontological metadata,
be they the annotations of an indexer or links forged between ontologies by an
ontology maintainer. However, existing systems may lack the means by which
to store arbitrary semantic constructs or even just URIs. For example, a mu-
seum indexing system may contain databases for museum items and actors, but
provide only a text field for storing locations. On the Semantic Web however,
locations need to be stored as instances with properties of their own.
To address this need, we have developed a browser-based metadata editor
SAHA, which can be used either as is or as depicted in figure 4, as a web
widget in existing indexing systems lacking semantic capabilities [27]. Connecting
SAHA to indexing systems can be done simply by linking to SAHA with a
GET parameter specifying the identifier (URI) of the document being currently
edited. By clicking on the link, a SAHA window opens, containing indexing
11
http://www.yle.fi/elavaarkisto/
12
http://www.google.com/adsense/
62
FIRST - First Industrial Results of Semantic Technologies
fields relevant for the current document. Afterwards, the annotations located in
the indexing system and SAHA can be combined by their common document
identifiers.
An indexing system missing URL
semantic annotation capabilities
URL
ONKI Browser
nt
om pone
-up c
mash
YSO ONKI-YSO-service
ontology SAHA annotation editor
Fig. 4. The Finnish General Ontology connected to SAHA.
3 Case Applications
In the following, we present how we have combined and applied the previously
described semantic web widgets in actual systems, both in content creation as
well as end-user consumption.
3.1 The ONKI Server: Publishing Ontologies as Web Widgets
Currently, ontologies are typically shared by downloading them, and each appli-
cation must separately implement the functionatily to support them. To avoid
duplicated work and costs, and to ensure that the ontologies are always up-to-
date, we argue that the ontologies should also be published as shared services.
To demonstrate this aproach, we have implemented the ontology library service
ONKI, which is used for maintaining, publishing and using ontologies [6].
As part of the ONKI concept, a user-interface web widget combining a seman-
tic autocompletion search sub-component with concept fetching functionality has
been implemented, which can be added to any application requiring access to
a certain ontology. The widget looks like an ordinary text field, but when the
user types in characters, matching concepts found from the ONKI server are
listed. By selecting a concept from the result list, the concept’s URI, label or
other information is fetched to the client application for further processing and
storage. In the context of a browser-based application, this fetching functionality
has been implemented with JavaScript window referencing [28].
63
FIRST - First Industrial Results of Semantic Technologies
Another ONKI feature is the concept browser, which can be integrated to
an application as an “ONKI button”. When the button is pressed, a separate
ONKI Browser window (see figure 5) opens, in which annotation concepts can be
searched for and browsed, making use of semantic autocompletion, tree context
visualization and concept property view components. For each concept entry, the
browser shows a Fetch concept button which, when pressed, transfers the current
concept information to the client application. For geographical data, a separate
browser application, ONKI-Paikka13 [29] has been created. This browser, shown
in figure 6, has been implemented by combining ontological information with
our geographical search and visualization widget.
Fig. 5. The ONKI Ontology Browser’s Fig. 6. The ONKI-Paikka browser.
concept view.
Integrating these ONKI services to client applications only requires a minimal
modification to the user interface implementation. For example, in the case of
HTML pages and AJAX, only a short snippet of JavaScript code must be added
to the web page for using the ONKI functionalities.
To test the ONKI solution, we have used the widgets in the browser-based
annotation editor SAHA14 [8, 27]. For example (figure 4), the Finnish General
Ontology YSO [6] has been published as an ONKI service15 , and has been added
as a web widget to SAHA for selecting annotation concepts. SAHA can also
make use of our automatic text extraction component in extracting potential
annotations from web resources.
In the case of the ONKI browsers, all concept and instance URIs are intended
to be designed so that they function also as URLs. When the URI of a concept is
accessed with a web browser, the relevant view is opened in the ONKI browser.
This means that the URI itself acts as a functional link when added to a HTML
13
http://www.seco.tkk.fi/services/onkipaikka/
14
http://www.seco.tkk.fi/services/saha/
15
http://www.yso.fi/onto/yso/
64
FIRST - First Industrial Results of Semantic Technologies
page. In accordance to W3C16 , if the URI is accessed with an RDF aware system,
the machine readable RDF presentation of the content is returned instead of the
ONKI browser’s HTML presentation. This makes it easier to use ONKI services
also in programmatic mash-up applications.
Compared to general ontology server interfaces such as the SKOS API17 , our
approach is to publish highly specified functionalities as semantic web widgets
that solve a specific user task, such as the need for concept search and fetch.
In this, our approach complements the general APIs. The general APIs make it
possible to create completely new functionality but require more programming
work, while semantic web widgets make handling the most common tasks as
cost-effective as possible.
3.2 CultureSampo: A Semantic Portal for Cultural Content
CultureSampo [30] is a semantic portal that gathers together a comprehensive
collection of Finnish culture, including photographs, paintings, poems and bi-
ographies. Much of the functionality of the portal has been accomplished by
combining the various components described above, as depicted in figure 7
Fig. 7. The mash-up architecture of the CultureSampo portal
First, we have harnessed our automatic concept extraction component to
enchance external web pages with CultureSampo content when viewed through
the portal. For example, on the left in figure 7, a web page from Wikipedia18 is
16
http://www.w3.org/TR/swbp-vocab-pub/
17
http://www.w3.org/2001/sw/Europe/reports/thes/skosapi.html
18
http://www.wikipedia.org/
65
FIRST - First Industrial Results of Semantic Technologies
integrated into the portal. The person names highlighted in blue on the page are
detected individuals, and their names are links to their biography in the portal.
On the right of the page, other recommended items based on the content of the
document are shown. These are calculated by feeding the concepts extracted
from the page to our recommendation component. CultureSampo also provides
multi-facet search functionality that utilizes our engine component combined
with both tree and map-based search and visualization views. This functionality,
along with a screenshot, is depicted on the right side in figure 7.
4 Discussion
4.1 Contributions
This paper presented the idea of using the mash-up approach for implementing
semantic functionalities as web widgets which can easily be included in applica-
tions, such as adding concept search functionality to an indexing application.
A major benefit of the approach is that potentially highly complicated and
expensive technical features and semantic data resources can be created once and
published for others to use in a compact package, which can easily be integrated
to an application. By making the adoption of semantic technologies as easy as
possible, one may hope to further the adoption of the Semantic Web as a whole.
One of the benefits of publishing the widgets as centralized services is that
updates in content and functionalities are instantenously available for the users.
This is an especially important feature when the data evolves constantly, e.g.
when user generated content is involved.
4.2 Related Work
Our own prior work on a semantic portal creation tool [31] already included
a general semantic view-based search tool [17] and the semantic linking service
Ontodella [24], as well as a framework for combining them into a complete portal.
However, the user interface components were not yet modular, and neither were
the search or recommendation functions used outside that environment.
On the other hand, many semantic web browsing and editing environments
do provide general configurable visualization and selection widgets inside them,
such as DBin [32], Piggy Bank [33], OntoWiki [34] and Haystack [35, 36]. These,
however, are intended for use only inside the specific program environment, while
our components are for general use.
Complementing our pursuits, there have recently been many announcements
about mash-ups that make currently existing data available in RDF. DBpe-
dia.org [37] has published Wikipedia material, the D2R server [38] has been
used for publishing the DBLP article database19 , while the RDF Book Mashup20
provides book information from Amazon and Google Base. From our viewpoint,
19
http://www4.wiwiss.fu-berlin.de/dblp/
20
http://sites.wiwiss.fu-berlin.de/suhl/bizer/bookmashup/
66
FIRST - First Industrial Results of Semantic Technologies
these mash-ups provide possible data sets to which our components could be
hooked. They provide the data, we provide the functionality.
4.3 Future Work
Future directions for this research include looking for new general semantic web
tasks that could be implemented using the web widget approach, especially
in ontology development and maintenance. For example, supporting cross-link
maintenance between ontologies, ontology change history maintenance and us-
ing ontology history knowledge in searches seem to be potential further research
directions. The proposed semantic web widgets could also be developed further.
For example, their functionalities could be provided via additional technologies
alongside the current JavaScript and SOAP Web Service APIs.
Acknowledgements
This work is part of the National Semantic Web Ontology (FinnONTO) project21 ,
funded mainly by the National Funding Agency for Technology Innovation (Tekes).
We wish to thank Ville Komulainen for his work on the ONKI Browser.
References
1. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American
284(5) (May 2001) 34–43
2. Hyvönen, E., Mäkelä, E., Salminen, M., Valo, A., Viljanen, K., Saarela, S., Junnila,
M., Kettula, S.: Museumfinland – finnish museums on the semantic web. Journal
of Web Semantics 3(2) (2005) 25
3. O’Reilly, T.: What is web 2.0 — design patterns and business mod-
els for the next generation of software. WWW article (September
30 2005) http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-
is-web-20.html.
4. Zhang, S.L.: Planning an authority control project at a medium-sized university
library. College and Research Libraries 62(5) (2001)
5. Niles, I., Pease, A.: Towards a standard upper ontology. In Welty, C., Smith,
B., eds.: Proceedings of the 2nd International Conference on Formal Ontology in
Information Systems (FOIS-2001). (October 17-19 2001)
6. Hyvönen, E., Viljanen, K., Mäkelä, E., Kauppinen, T., Ruotsalo, T., Valkeapää,
O., Seppälä, K., Suominen, O., Alm, O., Lindroos, R., Känsälä, T., Henriksson,
R., Frosterus, M., Tuominen, J., Sinkkilä, R., Kurki, J.: Elements of a national
semantic web infrastructure - case study finland on the semantic web. (May 18
2007) Submitted for review.
7. Mäkelä, E.: View-based search interfaces for the semantic web. Master’s thesis,
University of Helsinki (June 2006)
21
http://www.seco.tkk.fi/projects/finnonto/
67
FIRST - First Industrial Results of Semantic Technologies
8. Valkeapää, O., Alm, O., Hyvönen, E.: Efficient content creation on the semantic
web using metadata schemas with domain ontology services (system description).
In: Proceedings of the European Semantic Web Conference ESWC 2007, Innsbruck,
Austria, Springer (June 4-5 2007)
9. Hyvönen, E., Mäkelä, E.: Semantic autocompletion. In: Proceedings of the first
Asia Semantic Web Conference (ASWC 2006), Beijing, Springer-Verlag, New York
(August 4-9 2006)
10. Kauppinen, T., Henriksson, R., Väätäinen, J., Deichstetter, C., Hyvönen, E.:
Ontology-based modeling and visualization of cultural spatio-temporal knowledge.
In: Developments in Artificial Intelligence and the Semantic Web - Proceedings of
the 12th Finnish AI Conference STeP 2006. (October 26-27 2006)
11. Kauppinen, T., Deichstetter, C., Hyvönen, E.: Temp-o-map: Ontology-based search
and visualization of spatio-temporal maps. Demo track at the European Semantic
Web Conference ESWC 2007, Innsbruck, Austria (June 4-5 2007)
12. Ferraiolo, J., Jackson, D., Fujisawa, J.: Scalable Vector Graphics (SVG) 1.1 spec-
ification W3C recommendation. Technical report, World Wide Web Consortium
W3C (January 14 2003)
13. Kauppinen, T., Hyvönen, E.: Modeling and reasoning about changes in ontology
time series. In: Ontologies: A Handbook of Principles, Concepts and Applications
in Information Systems (January 15 2007)
14. Hildebrand, M., van Ossenbruggen, J., Hardman, L.: /facet: A browser for hetero-
geneous semantic web repositories. In: The Semantic Web - Proceedings of the 5th
International Semantic Web Conference2006. (November 5-9 2006) 272–285
15. Oren, E., Delbru, R., Decker, S.: Extending faceted navigation for rdf data. In:
International Semantic Web Conference. (November 5-9 2006) 559–572
16. Mäkelä, E., Hyvönen, E., Sidoroff, T.: View-based user interfaces for information
retrieval on the semantic web. In: Proceedings of the ISWC-2005 Workshop End
User Semantic Web Interaction. (Nov 2005)
17. Mäkelä, E., Hyvönen, E., Saarela, S.: Ontogator — a semantic view-based search
engine service for web applications. In: Proceedings of the 5th International Se-
mantic Web Conference (ISWC 2006). (Nov 2006)
18. Känsälä, T., Hyvönen, E.: A semantic view-based portal utilizing Learning Ob-
ject Metadata (August 2006) 1st Asian Semantic Web Conference (ASWC2006),
Semantic Web Applications and Tools Workshop.
19. Mäkelä, E., Viljanen, K., Lindgren, P., Laukkanen, M., Hyvönen, E.: Semantic
yellow page service discovery: The veturi portal. In: Poster paper, 4th International
Semantic Web Conference. (Nov 2005)
20. Sidoroff, T., Hyvönen, E.: Semantic e-goverment portals - a case study. In: Proceed-
ings of the ISWC-2005 Workshop Semantic Web Case Studies and Best Practices
for eBusiness SWCASE05. (Nov 2005)
21. Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R.: Semtag and seeker: Bootstrap-
ping the semantic web via automated semantic annotation. In: In Proceedings of
the 12th International World Wide Web Conference, ACM Press (2003) 178–186
22. Kiryakov, A., Popov, B., Terziev, I., Manov, D., Ognyanoff, D.: Semantic annota-
tion, indexing, and retrieval. Journal of Web Semantics 2(1) (2004) 49–79
23. Dzbor, M., Domingue, J., Motta, E.: Magpie: towards a Semantic Web browser.
Proceedings of the 2nd International Semantic Web Conference (2003)
24. Viljanen, K., Känsälä, T., Hyvönen, E., Mäkelä, E.: Ontodella - a projection
and linking service for semantic web applications. In: Proceedings of the 17th
International Conference on Database and Expert Systems Applications (DEXA
2006), Krakow, Poland, IEEE (September 4-8 2006) 370–376
68
FIRST - First Industrial Results of Semantic Technologies
25. Calı́, A., Giacomo, G.D., Lenzerini, M.: Models for information integration: Turn-
ing local-as-view into global-as-view. In: Proc. of Int. Workshop on Foundations
of Models for Information Integration (10th Workshop in the series Foundations
of Models and Languages for Data and Objects). (2001)
26. Ruotsalo, T., Hyvönen, E.: A method for determining ontology-based semantic
relevance. In: Proceedings of the 18th International Conference on Database and
Expert Systems Applications. (September 3-7 2007)
27. Valkeapää, O., Hyvönen, E.: A browser-based tool for collaborative distributed
annotation for the semantic web. (September 26 2006) 5th International Semantic
Web Conference, Semantic Authoring and Annotation Workshop, November, 2006.
28. Komulainen, V.: Public services for ontology library systems. Master’s thesis,
University of Helsinki, Department of Computer Science (January 2007)
29. Lindroos, R., Kauppinen, T., Henriksson, R., Hyvönen, E.: Onki-paikka: An on-
tology service for geographical data (2007) Submitted for review.
30. Hyvönen, E., Ruotsalo, T., Häggström, T., Salminen, M., Junnila, M., Virkkilä, M.,
Haaramo, M., Mäkelä, E., Kauppinen, T., , Viljanen, K.: Culturesampo–finnish
culture on the semantic web: The vision and first results. In: Developments in
Artificial Intelligence and the Semantic Web - Proceedings of the 12th Finnish AI
Conference STeP 2006. (October 26-27 2006)
31. Mäkelä, E., Hyvönen, E., Saarela, S., Viljanen, K.: Ontoviews – a tool for creating
semantic web portals. In: Proceedings of the 3rd International Semantic Web
Conference (ISWC 2004). (May 2004)
32. Tummarello, G., Morbidoni, C., Nucci, M., Panzarino, O.: Brainlets: ”instant”
semantic web applications. In Bizer, C., Auer, S., Miller, L., eds.: Proc. of 2nd
Workshop on Scripting for the Semantic Web at ESWC, Budva, Montenegro. Vol-
ume 183 of CEUR Workshop Proceedings ISSN 1613-0073. (June 2006)
33. Huynh, D., Mazzocchi, S., Karger, D.: Piggy bank: Experience the semantic web
inside your web browser. J. Web Sem. 5(1) (2007) 16–27
34. Auer, S., Dietzold, S., Riechert, T.: OntoWiki - a tool for social, semantic collab-
oration. In Cruz, I.F., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika,
P., Uschold, M., Aroyo, L., eds.: International Semantic Web Conference. Volume
4273 of Lecture Notes in Computer Science., Springer (2006) 736–749
35. Karger, D.R., Bakshi, K., Huynh, D., Quan, D., Sinha, V.: Haystack: A general-
purpose information management tool for end users based on semistructured data.
In: Proceedings of the CIDR Conference. (2005) 13–26
36. Quan, D., Huynh, D., Karger, D.R.: Haystack: A platform for authoring end user
semantic web applications. In: Proceedings of the Second International Semantic
Web Conference. (2003) 738–753
37. Auer, S., Lehmann, J.: What have innsbruck and leipzig in common? extracting
semantics from wiki content. In: Proceedings of the European Semantic Web
Conference ESWC 2007, Innsbruck, Austria, Springer (June 4-5 2007)
38. Bizer, C., Cyganiak, R.: D2R server — publishing relational databases on the
semantic web. Poster paper at the 5th International Semantic Web Conference
(November 2006)
69