=Paper=
{{Paper
|id=Vol-368/paper-2
|storemode=property
|title=Publishing and Using Ontologies as Mash-Up Services
|pdfUrl=https://ceur-ws.org/Vol-368/paper2.pdf
|volume=Vol-368
}}
==Publishing and Using Ontologies as Mash-Up Services==
https://ceur-ws.org/Vol-368/paper2.pdf
Publishing and Using Ontologies
as Mash-Up Services
Kim Viljanen, Jouni Tuominen, and Eero Hyvönen
Semantic Computing Research Group (SeCo)
Helsinki University of Technology and University of Helsinki
P.O. Box 5500, 02015 TKK, Finland
first.last@tkk.fi, http://www.seco.tkk.fi
Abstract. The Semantic Web is based on using ontologies for enabling
semantically disambiguated data exchange between distributed systems
on the web. This requires efficient means for publishing ontologies on
the web to ensure the availability, sharing and acceptance of the ontolo-
gies. Support services are needed for utilizing ontologies easily and cost-
effectively in applications and legacy systems lacking ontology support.
To address these vital needs, this paper presents the ONKI ontology
service which provides ready-to-use “mash-up” functionalities, such as
semantic disambiguation, concept finding and concept fetching as ready-
to-use web widgets for adding ontology support to e.g. HTML forms
using JavaScript. Two implementations of the ONKI Server are pre-
sented: ONKI-SKOS for ontologies presented in the Simple Knowledge
Organization System (SKOS) language and ONKI-Geo for geographical
ontologies with a map interface. The presented ONKI systems are oper-
ational on the web, used in the National Finnish Ontology Service. They
have been successfully used in several pilot applications.
1 Ontologies as Web 2.0 Services
The Semantic Web1 introduces a metadata layer on top of the World Wide Web
infrastructure for describing its content and services in an explicit, machine
“understandable” way using ontologies [1–3]. When such content is available,
semantically aware applications for e.g. searching and browsing the distributed
content can be created, as demonstrated in e.g. various semantic portals [4–
6]. Many ontologies have been created and are available online in RDF(S) and
OWL form today. For example, the Swoogle2 [7] search engine index contains
over 10,000 ontologies on the web.
One of the main lessons learned in our work on creating semantic portals [5,
8, 9, 6] is that metadata in data sources, such as museum databases, are often
syntactically heterogeneous and contain spelling errors, are semantically ambigu-
ous, and are based on different vocabularies [10]. This results in lots of tedious
syntactic correction, semantic disambiguation, and ontology mapping work when
making the contents semantically interoperable, and when publishing them on
1
http://www.w3.org/2001/sw/
2
http://swoogle.umbc.edu/
the Semantic Web. A natural solution to this problem would be to enhance
legacy cataloguing and content management systems (CMS) with ontological
annotation functions so that the quality of the original data could be improved
and errors fixed in the content creation phase. However, implementing such on-
tological functions in existing legacy systems may require lots of work and thus
be expensive, which creates a severe practical hindrance for the proliferation of
the Semantic Web.
This relates to the more general challenge of the Semantic Web today: on-
tologies are typically published as files without support for using them in appli-
cations. Each application tends to re-implement similar functions for utilizing
ontologies, such as semantic autocompletion and disambiguation [11], browsing
and finding concepts, and populating ontologies. It is like re-creating map ser-
vices from scratch in different geographical web applications, rather than using
available services such as Google Maps3 , Yahoo Maps4 , or Microsoft Live Search
Maps5 . We argue that ontologies should be published as lightweight shared ser-
vices which can be easily utilized in legacy systems using a mash-up approach in
the same spirit as e.g. Google Maps, Yahoo Maps and Freebase6 are used today.
This approach for publishing ontologies means, that generic, shared function-
alities are combined with specific applications using lightweight scripting and
programming technologies such as Ajax7 .
In the following, we first outline the requirements of mash-up ontology ser-
vices and present the implementation of a generic mash-up ONKI Ontology
Service and framework which is currently used in the National Ontology Service
in Finland8 . We then present two implementations of ontology specific server
implementations conforming to the ONKI Service framework: ONKI-SKOS for
general SKOS9 ontologies and ONKI-Geo [12] for geographical ontologies. Af-
ter this, utilization of ONKI services in an external application is discussed by
presenting two application scenarios. Finally, contributions, results, and lessons
learned are summarized, and directions for further research outlined.
2 Requirements of a Mash-Up Ontology Service
The national semantic web infrastructure model being built by the FinnONTO
project in Finland [13] argues that ontology services are needed for three major
user groups: 1) Ontology developers need a collaborative ontology development,
versioning, and publishing environment for ontologies [14]. 2) Content indexers
need services for finding the desired annotation concepts and for transporting
the corresponding URIs and other data from the ontology service into external
3
http://maps.google.com/
4
http://maps.yahoo.com/
5
http://maps.live.com
6
http://code.google.com/p/freebase-suggest/
7
http://en.wikipedia.org/wiki/Ajax (programming)
8
http://www.yso.fi
9
http://www.w3.org/2004/02/skos/core/
applications. 3) Information searchers need services for finding and disambiguat-
ing keyword meanings, and for transporting the corresponding URIs into search
engines and other applications. In this paper we focus on the problem of sup-
porting content indexing in applications and legacy systems.
Ontology servers are intended for managing ontologies, providing support
for designing, choosing and accessing ontologies [15–17]. However, compared to
previous work on ontology servers, we propose the idea of creating ontology
services which can easily be used in applications. This requires the following
features:
Mash-up integration support. Ontology servers should support runtime inte-
gration of the functionalities to applications and legacy systems, especially for
annotation and semantic search.
Semantic autocompletion and disambiguation. Efficient search functionalities
are important when trying to find the semantically correct concepts from large
ontologies. Text search boosted up with semantic autocompletion and disam-
biguation functionalities [11] supports the user in finding the right concept by
giving constant feedback of the query, and by helping in disambiguating the
intended concept meaning.
Concept fetching. When using ontologies in combination with other appli-
cations, the idea of “copying” or “transferring” concepts between applications
is important. We propose a concept fetching functionality for moving concept
URIs from the ontology server to the target application, such as a legacy cat-
aloguing system or CMS. To support legacy systems, indexing terms (concept
labels) should be possible to use instead of URIs even though this may create
disambiguation and mapping problems e.g. between ontology versions due to
potentially less specific identifiers than the URIs.
Concept collecting. Usually no single concept describes all the aspects of
the entity that is being described with a certain metadata property such as
dc:subject. Therefore, it should be possible to collect multiple concepts from the
ontology server and return these as a combination value in a specific metadata
field to the legacy system.
Domain-specific user interfaces. The concepts of an ontology are typically
visualized as an abstract graphical tree or graph visualization of the currently
selected concept with its semantic vicinity [18]. Complementing this, we propose
providing domain-specific interfaces, such as a map interface for geographical
ontologies, when applicable.
3 ONKI Ontology Service
The ONKI Ontology Service is a general ontology library and framework that
provides functionalities for accessing the ontologies using ready-to-use mash-
up web widgets as well as application interfaces for humans and machines for
doing, e.g., content indexing, concept disambiguation, searching and fetching.
The service is based on ontology and domain specific implementations of ONKI
Servers which conform to the ONKI interfaces. This means that it is possible to
provide a single mash-up web widget to access all ontologies but at the same time
provide domain-specific user interfaces and technical implementations optimized
for ontologies of different sizes, modelling languages, etc.
The ONKI Widget (Figure 1) is a ready-made user interface widget for using
the ONKI Service in content annotation (indexing). It enables the user, e.g. a
content annotator, to find the correct ontological concepts and their URIs and
then transfer the URIs and the concept labels to their own content manage-
ment application. Such a simple means for getting the URIs and to use them in
applications is crucial for enabling the content creation on the Semantic Web.
In the following, the JavaScript and Direct Web Remoting (DWR)10 based
implementation of the widget is described which is intended to be used for ex-
tending HTML forms with ontology functionalities. However, the proposed solu-
tion is more general because in the case of other user interface technologies such
as Java Swing, the ONKI Web Service11 interface could be used to implement
user interface technology specific implementations of the ONKI Widget.
Fig. 1. ONKI Concept Search Widget.
Part 1 of Figure 1 shows the default components of the widget. The ontol-
ogy selector can be used to change the ontology used in search or to select all
ontologies as target for the search. The search field is used for finding concepts
using text queries. In part 2 of Figure 1 the user is typing a search string to
the autocompletion search field which dynamically performs a query after each
input character (here “s-h-i-p-...”) to the ONKI service and returns the concepts
whose labels match the string, given the language selection. The results of the
query are shown in the web widget’s result list below the input field. In the case
of synonym terms, the preferred label of a concept will be presented. For exam-
ple, when searching for an (outdated) term “birch sugar”, the system returns
“birch sugar → xylitol” which means that “xylitol” is the preferred term.
When a desired concept is selected from the result list, it’s URI and label are
10
http://getahead.org/dwr
11
http://www.w3.org/TR/ws-arch/
put in the widget’s concept collector (Part 3 in Figure 1) for further usage such as
submitting the content to the server application or accessing the collector from
an application specific JavaScript program. The idea of the concept collector can
be compared to the idea of shopping carts in web stores. In the example, the
concept “ship travel” has been put into the concept collector.
The language of concept labels used in matching the query string can be
chosen by using the language selector. The choice of languages depends on the
ontology selected. For example, for YSO, English and Swedish are supported
in addition to Finnish, and the Finnish Geo-ontology12 can be used in Finnish,
Swedish, and in three dialects of Sami spoken in Lapland. It is possible to use
all languages simultaneously.
If the user doesn’t know what to type in the text search field, the alternative
of using a browsing interface is available by using the domain specific ONKI
Browser (“Open ONKI Ontology Browser” button). The ONKI Browser can also
be used for disambiguating homonym terms, i.e. concepts with identical labels,
by aiding the user to inspect the context of the concepts. When the desired
concept has been found using the ONKI Browser, the concept’s URI and label
are fetched into the application by pressing the ”Fetch concept” button on the
ONKI Browser page corresponding to the concept. Two implementations of the
ONKI Browser are presented in the next section of the paper.
The web widget can be integrated into an HTML Form with two lines of
JavaScript code. The following code line loads the ONKI JavaScript library and
should be added into the HEAD section of the HTML page:
Using the library, ordinary HTML form input fields can be extended with
ONKI functionality by declaring the onkeyup event handler for the field. For ex-
ample, adding the General Finnish Upper Ontology YSO to a example dc:subject
field is done as follows:
As a result, when a page is accessed, the user interface is enhanced with
ontology support. The widget provides a default concept collector (Part 3 in
Figure 1) which shows the fetched concepts in the widget’s user interface and
stores them in hidden input fields. When the form is submitted, the values of
the hidden input fields can be processed by the target application in the same
way as any HTML form submissions.
The ONKI Widget can be customized by configurations and by implement-
ing callback functions. Configuration possibilities include disabling the menus
for selecting ontologies and the language, the search field, or the “Open ONKI
Browser” button. The widget can also be configured to restrict the search to con-
cepts of certain type or belonging to a specific subtree of an ontology. Addition-
ally, CSS styling can be used for configuring the appearance of the widget. The
12
http://www.seco.tkk.fi/ontologies/suo/
ONKI Widget’s callback functions enable application specific implementations
of e.g. the concept collector or the concept search result list using JavaScript.
The ONKI API includes the following methods:
– search(query, lang, maxHits, type, parent) - for searching for ontological con-
cepts. Returns a list of hits.
– getLabel(URI, lang) - for fetching a label for a given URI in a given language.
– getAvailableLanguages() - for querying for supported languages of an ontol-
ogy. Returns a list of languages.
By implementing these methods, any system can be added to the ONKI Ser-
vice to be used via the general ONKI Service functionalities such as the ONKI
Widget. This is demonstrated by the case implementations presented below.
Thus, the ONKI Service is not tied to a single ONKI Server implementation.
4 Two domain specific ONKI Server implementations
Two domain-specific ONKI Servers have been implemented conforming to the
general ONKI service functionalities described in the previous section. ONKI-
SKOS is intended for lightweight ontologies and ONKI-Geo [12] for geographical
ontologies. In the following these two systems are shortly described.
4.1 ONKI-SKOS Server for SKOS Vocabularies
ONKI-SKOS is a general ontology service supporting thesaurus-like ontologies
especially in content indexing. ONKI-SKOS can be used to browse, search and
visualize any vocabulary conforming to the SKOS recommendation, and also
RDF(S) and OWL ontologies with additional configuration. ONKI-SKOS does
simple reasoning, e.g. transitive closure over class and part-of hierarchies. The
implementation has been tested using various ontologies, e.g. the General Finnish
Upper Ontology YSO, containing 20,000 concepts.
ONKI-SKOS Browser (Figure 2) is the graphical user interface of ONKI-
SKOS. It consists of three main components: 1) concept search with semantic
autocompletion, 2) concept hierarchy and 3) concept properties. When typing text
to the search field, a query is performed to match the concepts’ labels. The result
list shows the matching concepts, which can be selected for further examination.
The search can be further narrowed by restricting the search to concepts of a
certain type or to a desired subtree of the ontology.
When a concept is selected in ONKI-SKOS Browser, its concept hierarchy is
visualized as a tree structure, and its properties are shown as a table. Various
configuration properties are specified to enable ONKI-SKOS to process the on-
tologies as desired. The configurable properties include the ontological properties
used in concept hierarchy generation, the properties used to label the concepts,
the concept to be shown in the default view and the default concept type used
in restricting the searches.
Fig. 2. ONKI-SKOS Browser.
ONKI-SKOS Server is implemented as a Java Servlet using the Jena Semantic
Web Framework13 , the DWR library and the Lucene14 text search engine.
4.2 ONKI-Geo Server for Geographical Ontologies
ONKI-Geo [12] is an ontology service specialized for geographical data. It was de-
veloped for the Finnish Place Ontology SUO (Suomalainen Paikkaontologia) [19]
which currently has been populated with 1) place information from the Geo-
graphic Names Register (GNR) provided by the National Land Survey of Fin-
land15 and with 2) place information from the GEOnet Names Server (GNS)16
maintained by the National Geospatial-Intelligence Agency (NGA) and the U.S.
Board on Geographic Names (US BGN). GNR contains about 800,000 multi-
lingual resources of natural and man-made features in Finland, including data
such as place type or feature type and the coordinates of a place. The GNS
register contains similar information about 4,100,000 places around the world.
The ONKI-Geo Browser (Figure 3) is intended for annotating resources us-
ing unambiguous place identifiers (URIs) or coordinates for arbitary points or
polygons. Using unambiguous place identifiers is useful e.g. due to homonymous
place names: there are hundreds of places in Finland with the name “Isosaari”
(“Big Island”). The ONKI-Geo Browser contains several facets for narrowing
the search to find the intended place instance: A polygon can be drawn in the
map interface for making a search on all places in the selected area. The other
facets are an ontology of geographic features (e.g., lake, city, etc.), the languages
of the place names, and a place name search with autocompletion. The system
uses Google Maps widgets for visualizating the places.
13
http://jena.sourceforge.net/
14
http://lucene.apache.org/java
15
http://www.maanmittauslaitos.fi/
16
http://earth-info.nga.mil/gns/html/
Fig. 3. ONKI-Geo Browser. Search can be constrained by using the facets on the left
or by drawing a polygon on the map. By pushing the “Select” button in the left bottom
corner, the concept or selected coordinate information is transferred into the mash-up
widget.
5 Integrating ONKI with Application Systems
In the following we describe use cases of the ONKI system.
5.1 Integrating ONKI with a Cataloguing System
To demonstrate how to add ONKI functionalities to a legacy system, we created a
simple web form (part 1 of Figure 4) presenting the MuseumFinland [5] metadata
fields [20]. By adding the ONKI Concept Search Widget to the fields, ontologies
can be used in annotating museum collection items. Part 2 of Figure 4 depicts
the original form after adding the widgets.
After this the form can be used for creating semantic metadata. If the un-
derlying system does not support URIs, the system has to be modified to handle
this kind of information. Alternatively the ONKI Widget can be configured to
return concept labels instead of URIs.
5.2 An Annotation Editor Based on ONKI Ontology Services
SAHA17 [21, 22] is a generic annotation system supporting distributed collab-
oration in creating annotations, and hiding the complexity of the annotation
schema and the domain ontologies from the annotators. SAHA adapts to differ-
ent metadata schemas, which makes it suitable for different applications. Support
for using ontologies is based on ONKI ontology services (Figure 5). The system
17
http://www.seco.tkk.fi/services/saha/
1. The form without the ONKI widgets 2. The form after adding the ONKI widgets
Fig. 4. A museum cataloguing system before and after integrating the ONKI widgets.
is being tested in various practical semantic portal projects such as HealthFin-
land [6] and CultureSampo [23].
The metadata field elements are implemented using the ONKI widget, as dis-
cussed above. Depending on the field, different ONKI servers are used as specified
in the SAHA configuration. In this case the Finnish General Upper Ontology
YSO [24], published as an ONKI service18 , is used for selecting annotation con-
cepts. SAHA can also make use of the automatic text extraction component
POKA19 in extracting potential annotation concepts from web resources [21],
and populating the SAHA concept collector with them.
Annotations created with SAHA are stored in a centralized database, from
which they can be retrieved for editing or to be used in applications such as
semantic portals. It is possible to view and edit existing annotations by read-
ing the metadata fields in corresponding widget collectors. Furthermore, SAHA
supports population of its own annotation ontologies by new resources. In this
way, different users creating annotations collaboratively can share new resources
created by anyone, e.g., instances of new works of art or other artifacts.
6 Discussion
The main contribution of this paper is to present the idea of publishing ontolo-
gies as mash-up services that can be integrated in a lightweight fashion to legacy
systems on the user interface level. To demonstrate the applicability of the idea,
we presented the ONKI service and two implementations of the ONKI inter-
face: the general ontology server ONKI-SKOS and the geographical ontology
server ONKI-Geo. The two ONKI implementations also demonstrated the idea
of creating domain specific user interfaces to better support the usage of different
18
http://www.yso.fi/onto/yso/
19
http://www.seco.tkk.fi/tools/poka/
link
ONKI Browser
sion
ic inclu
Dynam NKI Widget
O
of the
ONKI-SKOS server
ontology
SAHA annotation editor
Fig. 5. ONKI integrated with the SAHA annotation editor.
types of ontologies. A practical contribution of the paper was to introduce the
idea of concept fetching between applications and the need for concept collecting
when using an ontology server for annotation purposes. Finally, semantic auto-
completion was proposed and implemented in the user interface components to
provide an efficient method for finding and disambiguating concepts.
A lesson learned from implementing the concept fetching functionality as
a web browser application was that special tricks are needed to transfer data
between browser windows loaded from different domains20 . Security being an
important concern, we suggest that browsers should provide some standardized
solution for communication between domains.
The widget is currently being developed for supporting other tasks where on-
tological concepts need to be searched and fetched, such as ontological content
search. Future work includes researching how the autocompletion concept search
could help even more in disambiguation the concepts without forcing the user
to open the ONKI Browser with all information about the concepts. In future,
other user interface environments than HTML and the web browser could be
supported, such as Java Swing. The ONKI API does not currently use RDF for
returning the content to the Widget to make it easier to handle the content with
JavaScript. However, in future RDF might be used. Finally, according to our
vision of a national ontology service, the ontologies in such a service should be
extensively and mutually interlinked to support creating cross-domain applica-
tions. Therefore, the question of how to support developing and using mutually
interlinked ontologies should be researched.
20
Since the ONKI-Browser is located in a different domain than the ONKI-Widget,
the communication between them was solved as follows: the Widget opens a new
browser window, which contains the ONKI-Browser in an IFRAME. The selected
concept URI is returned from the ONKI-Browser by changing the fragment identifier
of the window with the IFRAME, which can be accessed by the Widget.
Acknowledgments
We thank Ville Komulainen for his work on the first version of the ONKI server,
and Robin Lindroos and Tomi Kauppinen for collaboration in ONKI-Geo devel-
opment. Our research is a part of the National Semantic Web Ontology Project
in Finland21 (FinnONTO) 2003–2007 funded by the Finnish Funding Agency for
Technology and Innovation (Tekes) and 36 companies and public organizations.
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