=Paper=
{{Paper
|id=Vol-435/paper-3
|storemode=property
|title=Knowledge Representation for Web Navigation
|pdfUrl=https://ceur-ws.org/Vol-435/paper03.pdf
|volume=Vol-435
|authors=Simon Jupp,Robert Stevens,Sean Bechhofer,Yeliz Yesilada and Patty Kostkova
|dblpUrl=https://dblp.org/rec/conf/swat4ls/JuppSBYK08
}}
==Knowledge Representation for Web Navigation==
https://ceur-ws.org/Vol-435/paper03.pdf
Knowledge Representation for Web Navigation
Simon Jupp1 , Robert Stevens1 , Sean Bechhofer1 , Yeliz Yesilada1 , and Patty
Kostkova2
1
School of Computer Science, University of Manchester, Oxford Road, Manchester,
UK, M13 9PL.
2
City eHealth Research Centre, City University, London, UK
Abstract. Representations of domain knowledge range from those that
are ontologically formal, semantically rich to those that are ontologi-
cally informal and semantically weak. Representations of knowledge are
important in many tasks, one of which is the support of travel around
information spaces through the identification and linking of concepts in
a field. In this paper we explore how representations of ontologically in-
formal, semantically weak domain knowledge as captured by the Simple
Knowledge Organisation System (SKOS) can enable a system to take
advantage of the large number of existing ontological representations to
support semantic linking of Web based information and thus facilitate
information travel.
1 Background
We present an exploration of how background knowledge of biomedicine can
be represented to support the task of semantic linking of documents on the
Web. Bioinformatics and related disciplines rely upon Web based resources for
information gathering. Information can be gathered from the Web via document
retrieval using search, and document navigation via hypertext links embedded
into those documents. Despite the obvious success of the Web, it is not without
its limitations. Web users currently rely on search engines to retrieve documents
and once retrieved the information is presented for interpretation by humans
only. These documents may contain hypertext links to other documents on the
Web, but little information is given about the semantics of the link between two
resources.
Some of these limitations are being addressed with the use of Semantic Web
technologies [13]. The Semantic Web is an extension of the existing Web where
information is published in a representation that is interpretable by computers.
It is hoped a Semantic Web will provide an infrastructure to improve the way
we gather information from the Web.
Two key Semantic Web technologies endorsed by the World Wide Web Con-
sortium (W3C)3 are the Resource Description Framework (RDF)4 and the Web
3
http://www.w3.org/
4
http://www.w3.org/RDF/
Ontology Language (OWL)5 . RDF provides a base vocabulary for describing
resources and ad-hoc relationships between them. OWL is an extension of RDF
and provides a vocabulary for building ontologies. Ontologies are used to en-
code knowledge about a particular domain in the form of the entities and the
relationships between them. An ontology language like OWL has well defined se-
mantics, this facilitates computational interpretation of statements expressed in
OWL. It is hoped ontologies will provide the content to annotate documents on
the web, this Semantic Markup provides a mechanism for computers to interpret
a document’s contents.
Whilst ontologies offer great promise, the library and information sciences
have a history of using knowledge artefacts, known as Knowledge Organisation
Systems (KOS), to classify and index documents [22], [28], [21]. The KOS pro-
vide support for document retrieval and navigation applications. These KOS
can vary from simple dictionaries to more complex structures like thesauri and
controlled vocabularies, which introduce structure to the knowledge in the form
of associative relationships between concepts. The W3C have recently published
the Simple Knowledge Organisation System (SKOS)6 , a standard vocabulary
for representing KOS like structures. SKOS has a serialisation into RDF that
facilitates the use of SKOS in Semantic Web applications. OWL and SKOS may
appear similar at first, but have both been developed to fulfill different purposes.
Choosing which to use depends largely on application requirements.
The Sealife project7 seeks to develop a series of browsers in the context of
the Semantic Web and Semantic Grid [11]. The Semantic Web/Grid offers an in-
frastructure for large scale in silico science via a large number of computational
services. The Semantic Web/Grid settings and applications, however, need to be
combined with the continuing presence and use of numbers of Web documents
describing knowledge about biology. Ontologies and controlled vocabularies po-
tentially provide great benefits for describing and exploring the data in these
document resources as well as in the more usual avenues of annotation of data
and its subsequent analysis [3, 29]. The Sealife browsers aim to use these vocab-
ularies and ontologies as descriptions of knowledge in the life sciences to flexibly
manage the dynamic inter-linking of these documents and services. In this way,
a Sealife browser can couple standard modes of Web usage to the emerging
Semantic Web/Grid infra-structure.
The work presented here relates specifically to gathering the background
knowledge, in the form of ontologies and controlled vocabularies, to support
document navigation. We investigated the use of a KOS over a formal ontology in
providing background knowledge to support document navigation in a Semantic
Web browser like Sealife. The Conceptual Open Hypermedia SErvice (COHSE)
application is an implementation of a Sealife browser. We extended COHSE to
support SKOS style representation of knowledge; COHSE had previously worked
solely with OWL ontologies. We discuss the advantages of using SKOS over OWL
5
http://www.w3.org/TR/owl-features/
6
http://www.w3.org/TR/skos-reference/
7
http://www.biotec.tu-dresden.de/sealife/
in this application scenario and demonstrate the application in travelling the web
for resources on infectious disease using a KOS developed in SKOS.
2 Semantic Web Browsing
Navigation via hypertext is still the mainstay of the current Web [32]. Yet the
author owned and unary links of standard HTML frequently neither offer the
link sources nor targets needed by a particular group. The ability to browse
documents on the web via hyperlinks embedded in text is still a fundamental
part of the information gathering process used by biologists. As successful as
hypertext is, it is not without its limitations [5]:
Hard Coding: Links are hard coded into the HTML source of a document.
Ownership: Ownership of the page is required to place links in pages.
Legacy: A link target can be deprecated leaving invalid links on pages.
Unary targets: The current web links are restricted to point-to-point linking;
there is only one target for a link.
Consider this simple example; A document on the Web contains information
about Polio and the term Polio is marked up as a hypertext-link to another
document on the Web. This link is hard coded and is unary to the other doc-
ument, the target of this link is chosen and controlled by the owner of these
documents. If the document had some Semantic Markup, from a Knowledge
Base (KB) about human diseases, a Semantic Web browser could interpret its
content and offer services (or links) relating to Polio. By having background
knowledge about Polio, the browser also identifies Polio as having related terms
such as Polio disease, Polio virus, Polio symptoms, Polio immunisation,
and Polio treatment, again it could offer appropriate service based on these
terms. Having this background knowledge available to the Web browser offers
potentially new and beneficial avenues for exploration of information relating to
Polio.
This example is just one of many potential uses of Semantic Web technolo-
gies. Despite its potential, uptake of semantic technologies on the web has been
slow due to the large cost associated with developing KBs [12] and the subse-
quent cost of providing Semantic Markup to existing content on the web. The
number of KBs in the form of ontologies and vocabularies is growing consid-
erably, this is especially true in medicine and the life sciences [3]. To address
the issues of adding Semantic Markup manually, a range of semantic web ap-
plications emerged that used ontologies to automatically provide this markup
dynamically, at browse time. The main goal of these applications was to use
ontologies to identify a document’s content, and offer appropriate services dy-
namically. Some notable examples of these early Semantic Web browsers are
Magpie [9], KIM [23], Piggy Bank [14], AkTive-document [16], GoPubMed8 and
COHSE [5]. The Sealife project seeks to further this research on Semantic Web
8
http://www.gopubmed.com/
browser, Sealife has added new functionalities to both GoPubMed and COHSE
and investigated the use of Semantic Web browsers with use cases from the life
sciences.
3 The COHSE System
Conceptual Open Hypermedia supports the construction of hypertext link struc-
tures built using information encoded in ontologies [6]. Dynamic linking, sup-
ported by ontologies, offers a mechanism to help overcome some of the restric-
tions outlined above. The Conceptual Open Hypermedia SErvice (COHSE) sys-
tem (COHSE is available via here9 ) enhances document resources through the
addition of hypertext links (see Figure 1). These links are generated based on a
mapping between terms found in the document and lexicons available from the
ontology. Links can have multiple targets based on the type of concept identified
(see Section 6). In addition, the structure of the ontology facilitates navigation
to further targets based on sub/super concepts asserted in the ontology. Figure 1
shows a simplified view of the COHSE architecture, the browser interacts with
the Knowledge Service and a Resource Manager via the COHSE agent and DLS.
The COHSE architecture has been demonstrated in several fields, including
the GOHSE [2] system, which was an application in bioinformatics that used the
Gene Ontology (GO) [30] as an ontology. GOHSE would, for example, highlight
GO terms in a document; the user could then see the GO definition for that term
and be offered a menu of links that would perform either a PubMed Search;
take the user to the AmiGO browser10 ; browse GO associations11 etc.. The
Sealife project is now looking to extend this work and provide the background
knowledge that integrates many of the ontologies and other knowledge resources
being developed in biomedicine, to aid query by navigation to both scientists
and health care professionals in the study of infectious diseases.
4 Navigation requirements: Ontology or Vocabulary?
Much work has already been published on the requirements for a Semantic Web
browser in the context of document navigation on the web [5], [4], [10]. Here we
focus on three key requirements of a KB to provide the background knowledge
to support the task of navigation in a Semantic Web browser like COHSE:
1. The KB should provide rich lexical support for mapping terms in web doc-
uments to terms in the KB.
2. The KB should provide support for representing the relationships between
the terms. In particular the ability to generalise or specialise a given term.
3. The KB should be flexible enough to accommodate data from the many
other types of KBs that already exist or are under development.
9
http://cohse.cs.manchester.ac.uk
10
http://amigo.geneontology.org/cgi-bin/amigo/go.cgi
11
http://www.geneontology.org/GO.annotation.shtml
Fig. 1. COHSE architecture: Architecture of the COHSE system showing how a plain
web document is processed, the DLS uses the Knowledge Service and Resource Manager
to add hyperlinks to documents and provide new link targets.
Ontologies, like those represented in OWL, have been used to meet the re-
quirements of navigation in a Semantic Web browser. However, by restricting
these applications to ontologies, we are unable to take advantage of semantically
weaker structures, like thesauri, that also contain knowledge in structure that
have proven useful for navigation in the library sciences. On deeper inspection we
believe that semantically weaker structures actually provide a better “fit” for the
kind of navigation we want to support with COHSE. This is not to say that we
reject the use of formal ontologies or OWL within the COHSE system. Rather,
we have introduced an additional level of abstraction into the knowledge model
that allows us to implement the underlying knowledge structure using whichever
formalism we require (OWL, SKOS, etc.) while presenting a unified interface to
the user.
OWL is currently a standard recommendation by the W3C for representing
ontologies on the web. OWL provides a language for defining formlised concep-
tual models. It comes with well defined semantics that enable precise interpre-
tations of these models by logic based reasoners – reasoning can then be used
to help build and maintain consistent ontologies. These features are key for the
development of a Semantic Web that supports automated machine-to-machine
processing of data. OWL enables the data to be expressed explicitly and with
little or no representational ambiguity. However, such an approach comes at a
cost. OWL ontologies are complicated to build and maintain and require various
levels of expertise in not only the ontology’s subject matter, but also certain
aspects of philosophy and logic [24].
Given the fact that Semantic Web browsers are intended for human interac-
tion with the data, the relationships between concepts in the KB need not be
as strict as those represented in OWL. For example, consider navigating docu-
ments following the specialisation of some concept. Whilst the strict sub-super
class relationships in OWL can support this navigation, the semantics of this re-
lationship are not necessary for the task. Restricting these applications to OWL
ontologies alone means that modeling specialisations of concepts, that we as
humans may find completely intuitive, becomes difficult or even impossible in
OWL. In OWL, a subclass relationship means each instance of the subclass is
also an instance of the superclass and as such, is a strong statement about two
classes of instances. Specialisation and generalisation relationships need not be
describing this particular relationship between instances.
The examples given for polio above are a good example of this point. For a
further demonstration of this point we can look at a terminology that is com-
monly used in medicine – the Medical Subject Headings (MeSH) [20]. MeSH
is not an ontology, it is the National Library of Medicine’s (NLM) controlled
vocabulary thesaurus which is used to index articles from many of the worlds
leading biomedical journals. It consists of sets of terms organised in hierarchical
structures that permits searching at varying levels of specificity. The seman-
tics of the parent/child relationships between terms are not formally defined,
and are simply considered broader/narrower. For example, the semantics of A
hasNarrower B simply means that users interested in A might also be interested
in the more specialised topic B. The MeSH terms found under accident include
kinds of accidents—as expected e.g. Traffic accidents, but also Accident
prevention. This is not a good ontological distinction, but a valid one in the
context of navigation and retrieval.
The contrast in semantics means that conversions from MeSH into OWL
(with the broader/narrower relationships represented as subclass axioms) are not
possible without easily misinterpreting the intended semantics. From a naviga-
tion point of view, these weak semantics are acceptable and mean the background
knowledge can contain relationships between terms that may be unpalattable to
represent ontologically, but perfectly reasonable when we make the semantics
of the relationship similar to that of broader/narrower. The looser notions of
broader/narrower as found in vocabularies or thesauri provide the user with
weaker statements amounting to “this entity has something to do with another
more specialised/generalised entity”. This enables us to create knowledge bases
that contain the kinds of relationships between terms, that we as humans find
more intuitive in a context of navigation and retrieval.
One could argue at this point that a possible solution would be to sim-
ply introduce new vocabulary into OWL that represents the broader/narrower
relationships, but this introduces a requirement that the application machin-
ery be aware of these, resulting in a non-generic solution. SKOS does pro-
vide a recognised standard for representing these relationships often found in
KOS (broader/narrower/related). Although these relationships may not have
the precise semantics that comes with OWL’s relationships, in this context,
the looser interpretation is more appropriate to the task in hand. By enabling
COHSE to work with both OWL and SKOS style representations, we lose noth-
ing and benefit from being able to introduce a wider range of knowledge bases
that do not convert into OWL ontologies in a straight-forward manner.
4.1 The Simple Knowledge Organisation System
The Simple Knowledge Organisation System (SKOS) is a proposed standard
for representing and publishing classification schemes, thesauri, taxonomies and
subject heading systems on the web. It is currently under development as part
of the W3C Semantic Web Deployment Group (SWDWG) and a SKOS specifi-
cation has been published as a W3C last call 12 .
The SKOS model can be used to structure and represent knowledge artefacts
that contain statements about concepts and the relationships between them.
Thesauri, taxonomies, classification systems, subject heading and other similar
artefacts are considered to be different types of concept schemes, and often share
many features in common. These features are primarily in the form of a lexical
resource along with some semantic relationships between each resource. The
semantic relationships between resources are typified by synonym, hypernym,
hyponym, antonym, broader, narrower, related and so on.
One of the main goals of SKOS is to provide a simple and extensible model
that can be used to express these kinds of relationships between resources. SKOS
is designed to be extensible and modular. Central to SKOS is the core vocabulary
deemed sufficient to represent the common features found in concept schemes. A
concept can be considered any unit of cognitive thought, each concept has a set
of properties which include the lexical forms that describe it and its relationship
to other concepts. The class skos:Concept has instances which are the resources
being described in the concept scheme. Each resource will typically have some
preferred label (skos:prefLabel), some alternative labels which are considered
synonyms (skos:altLabel) and a definition (skos:definition). Concepts are
organised into hierarchies using broader-narrower relationships, or linked via
associative relationships.
Returning to the requirements for background knowledge in a navigation
system, we find that SKOS fulfills all three. SKOS has support for representing
a rich set of varied lexical information, which is useful for mapping term in
documents to concepts from the SKOS. The core semantic relationships in SKOS
allow us to express the informal relationships held between concepts that are
12
http://www.w3.org/TR/swbp-skos-core-guide/
deemed suitable for navigating a knowledge space. As SKOS provides a minimal
set of features to support the task in COHSE, it can be used as a representation
for knowledge bases that do not convert readily to OWL.
5 Gathering the background knowledge
For a Sealife browser such as COHSE to be useful across such a diverse subject
as biology we need a system to rapidly collect the current available ontology-like
resources together, and represent them using standard Semantic Web technolo-
gies. The biomedical domain already has a rich collection of vocabularies and
ontologies such as MesH [20] and all the many other vocabularies held within
UMLS [17], GALEN [26], the OBO ontologies13 , to name but a few. The lan-
guages used to represent these resources vary considerably, and can range from
simple taxonomy languages through to rich, formal logic based languages such
as OWL.
Converting existing terminologies into representation suitable for Semantic
Web applications can be difficult. Before SKOS, OWL was deemed the standard
for representing knowledge artefacts on the Web. Conversions from the MeSH,
GALEN and OBO formats into OWL exist, but this is non-trivial as many of
these formats do not have a precisely defined semantics like OWL. Care must be
taken when converting to OWL from formats where the semantics are weaker
than those found in OWL. In contrast, the weak semantics of SKOS make it
possible to convert a wide range of artefacts into SKOS, making the artefacts
available to SKOS aware applications. The conversion is potentially lossy, espe-
cially when converting from OWL to SKOS, but where it is simply the structure
that is necessary for the task and not the semantics that can drive sophisticated
inference, such loss is acceptable. In addition, of course, with such a conversion,
the original artefact still exists in its original representation. Providing both
SKOS and OWL representations of knowledge bases is perfectly reasonable and
should be encouraged.
Conversions into SKOS already exist for the MeSH thesaurus [31]. For the
Sealife project, we produced a range of converters for many representations in-
cluding the OBO format14 ; OWL; and the vocabularies in the UMLS (The OBO
to SKOS converter is available online15 ).
5.1 OBO to SKOS conversion
Many of the ontologies and controlled vocabularies being developed in the life
sciences are done through the OBO foundry [1]. The OBO foundry provide a
principled set of requirements for building ontologies in the OBO format, they
also provide a set of relationships that OBO developers are encouraged to use.
These relationships do not have the strong model-theoretic semantics of OWL,
13
http://obo.sf.net
14
http://www.geneontology.org/GO.format.shtml
15
http://www.cs.man.ac.uk/∼sjupp/skos/index.html
but they are precise and described in natural language in the Relations Ontology
(RO) [27]. The OBO language does not have the same level of expressivity as
OWL, but it does benefit from many features that are not available in OWL,
such as rich annotation support. Conversion from the OBO format into OWL
exist [19] making integration of OBO ontologies with Semantic Web applications
possible. There are currently over 54 OBO ontologies being actively developed,
providing a great coverage of domain terminology used in biology. Many of these
terminologies benefit from having rich labeling support and textual description
for each term. Providing a SKOS representation of the OBO ontologies along
with an OWL version means more Semantic Web applications that are SKOS
aware can benefit from this rich terminological resource. In the use case (Sec-
tion 6) we also demonstrate how having a SKOS style representation of OBO
ontologies enables developers to reuse sections of OBO terminologies when build-
ing their own vocabularies in SKOS.
Here we present the outlines for a conversion of the OBO format into SKOS.
When converting OBO ontologies into SKOS we can take the relationships de-
fined in the RO and map them to the semantically weaker notions found in
SKOS to assert broader, narrower and related relationships between SKOS
concepts. Here is an example of the conversion one might make when mapping
ontological properties to SKOS properties.
– OBO REL:part-of -> skos:broader (e.g. finger part of hand)
– OBO REL:contains -> skos:narrower (e.g. skull contains brain—ignoring the
cabity for now)
– OBO REL:adjacent-to -> skos:related (e.g. nuclear membrane adjacent-to
cytoplasm)
Another advantage when converting properties from ontologies to SKOS is
the ability to assert the inverse. Consider an ontology where Nucleus partOf
cell, from an ontological point of view this implies that each and every Nucleus
is partOf some Cell. The inverse, however, is not true, every Cell does not
hasPart Nucleus. When converting to a SKOS model we can assert the inverse
using the broader property to say that Nucleus has a broader term called Cell,
which is reasonable. When navigating around documents about cells, the system
could then also provide links to documents about nuclei—users interested in cells
are often also interested in nuclei.
6 Use Case Scenario
One example application for the COHSE Sealife browser is to provide dynamic
hyper-linking of resources from the National electronic Library of Infection (NeLI)
[15] portal (http://www.neli.org.uk) to other related resources on the web. NeLI
is a digital library bringing together the best available on-line evidence-based,
quality tagged resources on the investigation, treatment, prevention and control
of infectious disease. Many documents on the NeLI site contain few, if any, hy-
perlinks to other resources on the web. It would take a large curational effort
and cost to manually mark up and maintain these pages with links to other web
resources. In addition to this problem, NeLI has a range of users; different link
targets are required based on the kind of user browsing the NeLI site. This exam-
ple shows how the SKOS representation affords the navigation we need and also
shows that building a new vocabulary in SKOS can be done rapidly by reusing
portions of a variety of controlled vocabularies that have already been converted
into SKOS (e.g. MeSH and OBO).
The ability to identify user groups is important. Users can range from mem-
bers of the public, molecular biologists, to clinicians and family doctors. Each
group has a different perspective on the bio-medical domain, and is therefore in-
terested in different kinds of information. By providing alternative vocabularies
for different users, the system can identify link sources relevant to that user and
also provide multiple targets to relevant web resources. Table 1 shows four dif-
ferent user groups, some questions they might want answering and the different
kinds of target sites a Sealife browser would offer them based on user type [18].
User Group Question Targets Sites
Family Doctor Tuberculosis drugs and side ef- British National Formulary (BNF)
(GP) fects?
Clinicians Tuberculosis treatments Public Health Observatories
guidelines? (PHO)
Molecular Biolo- Drug resistant tuberculosis PubMed
gists species?
General Public What is tuberculosis? Health Protection Agency (HPA)
or the NHS direct online website.
Table 1. This table shows some of the different user groups that visist the NeLI site,
they all have different types of question they want to answer about the same concept.
The table outlines different targets COHSE would provide to the user based on the
user group they belong.
The system is demonstrated with a simple use case involving a news site
linking to NeLI. News sites are often the first to report on disease outbreaks via
news feeds. Consider the scenario where a traveller is planning a trip to Namibia,
only to find an article on the BBC website16 about a recent outbreak of Polio.
COHSE can provide links to relevant resources that had not been included by the
original author. Such resources could include information about the polio virus,
its effect on humans, vaccination information and also geographical information
about the local area. A family doctor, in contrast, might use a vocabulary skewed
to the their interests to link through to sites on drugs, details of symptoms and
clinical presentations, treatment and local hospital facilities etc..
Figure 2 shows the COHSE system in action. The first image shows the
original BBC article, this page has the links provided by the original author of
16
http://www.bbc.co.uk
the page, these include links to related news stories and also a link to the World
Health Organisation. Whilst these links may be useful to a user, it is easy to
imagine a wide range of related information one might want to get to from this
page. In this scenario the user wants information on polio vaccination. The BBC
article contains no direct link so the user would be forced to use a search engine.
Querying polio vaccination against a search engine would typically bring a
wide range of documents that contain the word polio and/or vaccination,
the user must manually filter through these documents to find the information
they require. The search engine will not discriminate between news article, fact
sheets or clinical guidelines, neither will it provide any indication as to who are
the world authorities on vaccination information. All of this must be processed
by the user which takes time and effort. There is also the problem that the search
engine will miss documents that do not mention polio directly. The documents
might mention poliomyelitis, which is the clinical term for the disease caused
by polio virus. These documents may be vital to the user but missed because of
the language used in the query.
Fig. 2. COHSE in action: Three screen shots showing COHSE adding hyperlinks to
a news article, providing multiple link targets and finally the new page with more
concepts highlighted.
The COHSE system can help the user in this scenario by identifying the
key concepts in the documents and offering relevant links from the current site
based on related terms and synonyms. When viewing the BBC article through
the COHSE portal the document is first processed by the COHSE DLS (see
Section 3). In this scenario the COHSE Knowledge Server (KS) has been loaded
with a SKOS vocabulary that contains terms relating to infectious disease (the
vocabulary was developed at NeLI and is represented in SKOS17 [7]). The DLS
identifies concepts in the document based on queries against the KS, the original
document is returned to the user with the concepts highlighted and hyperlinked.
The term Polio in the document has been identified as a synonym of the term
Polio Virus and highlighted. The user can now select this new hyperlink to see
what link targets the COHSE system is offering.
When the polio link is selected, the term is dynamically queried against
the COHSE Resource Manager (RM). In this scenario the RM has been loaded
with five web-services. These web services have been selected by experts from
NeLI as being authoritative sites on information about infectious diseases. The
term polio along with broader, narrower and related terms from the concept
scheme are queried against the RM with the most relevant results returned as
link targets to the user. In this example we see that Polio Vaccination is
related to the term Polio via the narrower relationship. Polio Vaccination
when queried against the RM returned links to a document from NeLI with
guidelines about polio immunisation. The second screenshot (Figure 2) shows
the links box offered to the user with links relating not only to polio, but to all
the related terms found in the concept scheme. As the user continues to navigate
around the web, the COHSE system continues to identify concepts in web pages
and offer appropriate hyperlinks.
One important aspect to guiding navigation in this way is that the modality
is kept, having to switch between windows and search engine results breaks the
modality and can interfere with the task at hand [8]. We believe that having a
selection of relevant links on hand in the current window adds value to the user
experience.
The infectious disease vocabulary used as background knowledge for the NeLI
use case has been developed in SKOS [7]. There already exist a wide range of
vocabularies that cover the domain of infectious disease, these include the OBO
Disease ontology18 and MeSH. NeLI would like to reuse terminology from both
to drive the COHSE system on their site, but they also want to extend these
vocabularies with specific terms from their own internal vocabulary. NeLI do not
wish to model the domain of infectious disease ontologically, they need a flexible
vocabulary that contains the domain concepts and information about synonyms
and related concepts, which can be used to drive navigation around their site
and others on the Web. SKOS meets their requirements, and by having SKOS
representation of both the MeSH and the OBO Disease ontology they are able to
integrate these existing resources, and extend them with their own vocabulary
for use within the COHSE system. Having to combine these three vocabularies
in OWL would not only involve violating the OWL semantics, but is simply
17
http://www.cs.man.ac.uk/∼sjupp/ontologies/NeLI-demo.xml
18
http://diseaseontology.sourceforge.net/
unnecessary for the task. The NeLI vocabulary not only demonstrates the use
of SKOS in driving Semantic web browsers like COHSE, but also shows how
a knowledge base with weak semantics like SKOS makes it easier to integrate
a wide range of differing resources in applications where weaker semantics are
acceptable.
7 Discussion
The advent of the Semantic Web/Grid has brought large computational resources
to bear upon knowledge intensive sciences such as biology. Despite this, user
facing tools that support document orientated tasks are not yet available within
this new paradigm for computing. Semantic technologies can be applied to this
problem to overcome some of the limitations of the current Web’s navigational
structure. We have argued that a representation with weaker semantics, such
as SKOS, will enable us to exploit the wealth of ontologies, thesauri and other
types of knowledge organisation schemes already existing within the biomedical
domain.
Representations with stricter semantics are not always suitable for repre-
senting artefacts such as MeSH. KOS such as MeSH were created, not to make
ontological descriptions of biological reality, but to aid navigation and retrieval
of information about biomedicine.
The nature of formal ontologies can sometimes make it difficult to express re-
lationships between concepts that experts from the domain would expect to find
under some circumstances, such as in information retrieval tasks [25]. Thesauri
are suited to represent the way words and language are used in the field. The
Sealife project will demonstrate how the effort and cost associated with building
the kinds of rich formal ontologies that are represented using OWL, can feed
into other knowledge artefacts, like thesauri, vocabularies, classification schemes
etc.. in SKOS, which are then available for use in different application scenarios.
Acknowledgements
Funding by the Sealife project (IST-2006-027269) for Simon Jupp is kindly ac-
knowledged. Yeliz Yesilada was supported by Sun Microsystems Laboratories.
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