=Paper=
{{Paper
|id=Vol-518/paper-7
|storemode=property
|title=Towards Modelling the Intended Purpose of Ontologies: A Case Study in Geography
|pdfUrl=https://ceur-ws.org/Vol-518/terra09_submission_9.pdf
|volume=Vol-518
}}
==Towards Modelling the Intended Purpose of Ontologies: A Case Study in Geography==
https://ceur-ws.org/Vol-518/terra09_submission_9.pdf
Towards Modelling the Intended Purpose of
Ontologies: A Case Study in Geography
Ronald Denaux1 , Anthony G. Cohn1 , Vania Dimitrova1 , and Glen Hart2
1
School of Computing, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK
r.denaux@leeds.ac.uk
2
Ordnance Survey Research, Romsey Rd, Southampton, SO16 4GU, UK
Abstract. As the Semantic Web technologies gain popularity, more or-
ganisations are providing semantic resources related to the geographi-
cal domain but are doing this based on ontologies that represent their
own conceptualisations and needs. As a result: finding, understanding
and evaluating relevant semantic resources is still very time consuming,
cognitively demanding and often require a deep understanding of the do-
main and the used semantic technologies. To help address this problem,
we are investigating how ontology purposes can be captured and used to
facilitate ontology engineering. The paper presents an exploratory study
which examines whether it is possible to identify generic categories to
model the purpose of geography-related ontologies. As an output, we
present an initial categorisation of ontology purposes and discuss how
these categories can help us understand and reuse ontologies.
1 Introduction
As the Semantic Web technologies become more mainstream, more people and
organisations are creating and publishing conceptualisations (using OWL) and
descriptions of their data (using RDF). In the spirit of the World Wide Web, the
technologies allow for each user to publish his own data and conceptualisations,
while making it easy to interlink these semantic resources with those of other
people. However, a big difference between the WWW and the SW is that in the
WWW it is relatively easy to understand websites made by other people if you
share the same natural language. In the SW the tasks of finding, understanding
and evaluating relevant semantic resources are still very challenging. These tasks
are currently time consuming, cognitively demanding and often require a deep
understanding of the represented domain and the semantic technologies used.
Much research is finding ways to alleviate these problems by providing seman-
tic search [1, 2] and making it easier to understand ontologies by, for example,
identifying the main concepts [3], providing vocabularies to annotate them [4]
and studying their usage [5]. Our aim is to contribute to these research efforts
by studying the stated purpose of ontologies.
This paper describes our first investigations in the area of ontology purposes,
based on the geography domain. This domain presents a good use case for our
investigation as the problem of finding and correctly re-using suitable seman-
tic resources has already been reported [6]. These problems are partly caused
by a a common practice where different organisations are producing their own
geographic resources based on different conceptualisations and needs in reponse
to (i) a need for interlinking geographic data with many different domains (e.g.
agriculture, environment); (ii) the availability of large amounts of geographic
data and (iii) a relative ease of data production as most people have at least
some basic geographic knowledge3 .
The motivation for this work is to provide a high-level understanding of se-
mantic resources by using the stated purpose. This is in line with other benefits
of purpose descriptions pointed out by prominent ontology construction method-
ologies[7, 8], which regard the definition of the ontology purpose as a fundamental
steps in the ontology development. The purpose helps to set the scope of the
ontology and to evaluate whether it is fit for purpose. Furthermore, agreeing
on a common ontology purpose improves collaborative ontology development
by allowing ontology developers to refer to the stated ontology purpose to re-
solve modeling issues [9]. Thus, having a way to describe and formally represent
ontology purposes could enable tool support for ontology construction.
Despite the possible advantages, we are not aware of a suitable way to for-
mally define the purpose of ontologies. Where a purpose is defined it is done
so as free text and rarely distributed with the ontology. Competency questions
(CQs) [8] —questions that the ontology is required to answer— have been pro-
posed to capture ontology requirements and tool support to formulate [10] them
is available. However, CQs (i) can only refer to entities that are defined by the
ontology itself, while purpose descriptions often need to refer to entities outside
of the scope of the ontology in order to put the ontology into a wider context;
(ii) are not commonly distributed with the ontology; (iii) can only be formalised
as queries at a low granularity level, so the overall purpose of the ontology may
not be clear based on the total set of CQs (especially for large ontologies) and
(iv) cannot be organised in a hierarchy of purposes.
This paper presents an exploratory study which examines whether it is pos-
sible to identify generic categories to model the intended purpose of a corpus of
ontologies that use geographical concepts. To find an answer to this question, we
obtained a corpus of semantic resources as described in Section 2. In Section 3,
we describe how the corpus was analysed in order to obtain a categorisation of
the ontology purposes. We discuss some findings of our study in Section 4 and
finish with a description of our future work in Section 5.
2 Corpus Collection
To obtain a corpus of ontologies, we considered a common ontology engineering
scenario where a domain expert with limited knowledge engineering experience
is looking for a concept to reuse in a new ontology. In particular, we decided to
3
This is demonstrated by, for example, the OpenStreetMap project. http://www.
openstreetmap.org/
use the concept River as this is a common concept which will ensure that we
find a large number of ontologies that we can add to our corpus. Furthermore,
River is a central concept in the Ordnance Survey Hydrology Ontology4 , which
we can use as a baseline ontology.
2.1 Finding Semantic Resources to Create a Corpus
In order to find semantic resources based on our seed concept River, we fol-
lowed recent research that advocates using semantic web search engines to find
ontologies published on the web instead of minting new concepts [11]. As a re-
sult to our query for semantic resources based on River, Sindice [2] reports over
204×103 results, Watson [11] reports over 1500, while Swoogle [1] finds 302 re-
sults5 . We also use used Google to find semantic resources by restricting our
search to filetype .owl (572 results).
The large number of results for our query required further filtering until
we obtained ontologies defining the concept River. Sindice found the biggest
number of semantic resources, but most of the results were RDF documents
describing instances from large datasets such as DBPedia and Geonames. For
example, http://dbpedia.org/resource/Parramatta_River and http://dbpedia.
org/resource/Cry_Me_a_River. Finding River definitions (not instances) with
sindice is difficult because there is no filtering interface at sindice.com to navi-
gate through the search results.
The results from Watson contained ontologies in a variety of formats (e.g.
RDF(S), OWL, DAML+OIL). Watson does not allow filtering or sorting of
search results either, so we browsed through the ontologies using the ontol-
ogy URIs and metadata such as the size of the ontology in Kilobytes, number
of classes, properties and individuals, type of ontology (e.g. RDF, OWL) and
OWL-DL expressivity (e.g. SHOIN ). This information aided in discovering du-
plicates (the same ontology is hosted on different servers) or results which are
not useful such as foaf user profiles, describing locations(e.g. Fall River) and
interests (e.g. River Dancing).
Swoogle’s results are similar to Watson’s with the added advantage that
Swoogle provides options for ranking the results based on ontoRank (their own
ranking system), date or triple.
Google’s results included several ontologies that contained a comment with
string River but did not define a River concept. Also, some files with extension
owl were not OWL files. Google results only provided text snippets and the
ontology URI, but do not include OWL-specific metadata.
2.2 Finding Ontologies with a Purpose Description
Our study required that the ontologies in the corpus should have a purpose
description. Initially, we searched for ontologies that define a purpose annotation.
4
http://www.ordnancesurvey.co.uk/oswebsite/ontology/Hydrology/v2.0/
Hydrology.owl
5
data gathered in June 2009
However, the number of ontologies that contained such an annotation was very
limited, so we decided to also collect generic descriptions about the ontology. In
the absence of a standard way of describing ontologies, we used three additional
sources of purpose descriptions; which we describe below.
First, we searched the website hosting the ontology for an ontology purpose
description. In many cases, the website provided a description of the ontologies.
This description sometimes included an explaination on why the ontology had
been created which we used as a purpose description (e.g. the GWSG ontology).
We also searched the serving website for a project purpose description. In
some cases, when the website served multiple ontologies, it did not provide a
description for each separate ontology (e.g. NASA SWEET). In these cases,
we looked for a more generic description of the website as a whole or of the
project that produced the ontology. If this generic description included a purpose
description—and it was clear that the ontology was created as a means to achieve
that purpose—, we included the description for the ontology.
Finally, we searched for publications that explain a project in more detail.
We conducted this step when the website did not contain a suitable purpose
description, but was part of a project for which publications could be found. We
then searched the publications for descriptions of the ontology and how it was
used in the project. For example, the purpose description of the German Geo
KB, was gathered from (i) a description of the ontology in [12]; (ii) a description
on why the ontology was used [12] and (iii) a description of the project in [13].
2.3 Corpus Description
The corpus collected consists of 9 OWL ontologies with a free text description
of their intended purposes and the provenance of the purpose description (see
Table 1). The ontologies in the corpus range from small knowledge bases such
as German Geo KB to relatively large ontologies like NASA SWEET.
The purpose descriptions in the corpus came from a variety of places, as
described above. In the best of cases, the authors provided a succinct description
of the ontology purpose (e.g. Ordnance Survey Hydrology Ontology in Table 2.
In the worst cases, the purpose of the ontology has to be inferred from documents
describing the project that created the ontology (e.g. [14] describes the BOEMIE
project objectives and includes sentences that hint at the purpose of the GIO
ontology (see Table 2).
3 Purpose Analysis, Conceptualisation and Formalisation
The presence of ontology (and project) descriptions that did not describe the
ontology purpose explicitly posed a problem: modeling these implicit purpose de-
scriptions was a subjective task that required an interpretation of the documen-
tation based on assumptions. In order to minimise this effect and to keep track of
Table 1. Overview of the ontologies in the corpus including ontology metrics (all
gathered using Protégé 4.0), type of the source of the ontology purpose description
and the organisation that created the ontology.
Ontology Purpose DL Metrics
Ver. Organisation
Name Source Cs OPs DP Inds SubC EqAx DisAx Anns
Ordnance 2 Ont. Anno- Ordnance ALCHOIQ 194 43 0 16 329 27 2 479
Survey tation Survey
Hydrology
NASA 2 Tech. Doc. NASA SHOIN (D) 1834 130 25 139 2119 208 19 381
Sweet Hy- & Website
droBody-
OfWater
GIO ontol- 2.5 Tech. Doc. BOEMIE ALCHN (D) 291 52 29 0 364 0 12 245
ogy project
Germany n/a Research University of ALQ(D) 12 17 18 634 59 0 0 0
Geo KB Publication Karlsruhe
EnvOc 1.5 Website envoc.org ALE+ 1233 4 0 0 1348 0 0 5933
GWSG 1 Ont. An- MUSIL Uni- SHIN 350 215 0 2 960 0 21 462
notation & vesitt Mnster
Website
E-response n/a Ont. Anno- e- SHOIN (D) 1746 182 19 323 2147 73 1266 941
tation response.org
WOW n/a Website & walkonweb.orgALCN (D) 55 50 50 127 46 0 0 143
Tech. Doc.
hydroseek n/a Tech. Doc. hydroseek.org AL 16 0 0 0 15 0 0 15
navigation
Cs = Class Count OPs = Object Property Count DP = Data Properties Count
Inds = Individual Count SubC = Subclass Axiom Count EqAx = Equivalence Axiom Count
DisAx = Disjoint Axiom Count Anns = Entity Annotation Count Ver. = Ontology Version
Table 2. Examples of free text purpose descriptions, their source and corresponding
purpose phrase
Description Source Purpose phrases Code
Purpose: To describe in an unambiguous manner Ontology Describe the inland hy- OS1
the inland hydrology feature classes surveyed by Annotation drology feature classes sur-
Ordnance Survey with the intention of improving of Ordnance veyed by Ordnance Survey
the use of the surveyed data by our customers Survey in an unambiguous man-
and enabling semi-automatic processing of these Hydrology ner.
data. Ontology Enable semi-automatic OS3
processing of the data
surveyed by Ordnance
Survey.
Driven by domain-specific multimedia ontologies, BOEMIE Provide domain-specific GIO1
BOEMIE information extraction systems will be project background knowledge
able to identify high-level semantic features in descrip- To use in information ex- GIO2
image, video, audio and text and fuse these tion [14] traction tasks in multi-
features for optimal extraction. [...] The rationale media
behind this approach is that multimedia To enable identification of GIO3
information extraction can benefit in many ways high-level semantic feature
from the background knowledge provided by in image, video, audio and
ontologies [...] text
the assumptions made during the analysis of the ontology purpose descriptions,
we used a Grounded theory-based approach [15] for analysing the corpus. Fig-
ure 1 shows an overview of the steps taken during the analysis to arrive at a con-
ceptualisation.
As a first step towards conceptualistion,
we identify and extract purpose phrases
based on the ontology descriptions in
the corpus. These are simple statements
that describe a single goal that the on-
tology should help to achieve. To com-
pose the purpose phrases, sections of free
text descriptions have to be rephrased
(we try to stay as close to the origi-
nal text as possible to minimise the in-
troducing assumptions). As an exam-
ple, Table 2 shows some purpose phrases
based on purpose descriptions. The ta-
ble also shows that each purpose phrase
is linked to a unique code that links the
purpose phrase to its originating ontol-
ogy and free text description. Due to
space limitations we cannot show all the
purpose phrases6 .
The next step in the analysis is to Fig. 1. Overview of steps to obtain an
make sure that the purpose phrases have initial ontology of purposes for geo-
a common structure that represents an graphic ontologies based on free text
ontology purpose. This structure has purpose descriptions
been inspired by existing work describ-
ing goals in the medical context [16]. Al-
though there is no standard terminology for describing goals, [16] states that
most goals can be decomposed into a tuple consisting of a task and a focus. This
tuple can be extended with optional components such as restrictions, a situation
description, a policy definition (e.g. whether goal is obligatory) and a rationale
(i.e. clarification of the goal). For our purpose phrases (i) the situation descrip-
tion is always the same: the creation of an ontology; (ii) no policy definition is
necessary and (iii) the rationale is the purpose description source. So we can
decompose our purpose phrases (e.g. OS3 in Table 2) into a task (e.g. Enable), a
focus (e.g. semi-automatic processing of data) and zero or more restrictions(e.g.
the data has been surveyed by Ordnance Survey).
The task is the verb phrase at the beginning of each purpose phrase. For
example, the task for GIO1 is Provide and the task for OS3 is enable (see Table 2).
Defining a single best focus and a set of restrictions is not always easy. For
6
At the moment of writing we have identified 63 purpose phrases. A
full table can be found at http://spreadsheets.google.com/pub?key=
tkeezO29qTGOVZ1Rd41JbTw&gid=2
purpose phrase OS3, the focus can be semi-automatic processing of data (plus
some restrictions), or it can be processing of data (where the fact that this
processing is semi-automatic is a restriction)7 .
We found generic concepts to use in our categorisation by making the focus of
each purpose phrase as abstract as possible and expressing as many restrictions
as possible. The restrictions of the purpose phrases define specialisations of the
generic concepts in the categorisation. After refining these generic categories we
arrived at 7 types of purposes:
1. Domain Defining: these purposes occur when there is a need to concep-
tualise a domain. They usually restrict the scope of the ontology and may
also impose restrictions on how concepts are represented. An example of this
is gwsg7:Find an ontological consistent description of the qualities of a data
set of observations.
2. Ontology process related: these purposes state that the ontology should
influence (enable, improve, etc.) ontology processes such as the use, re-use,
merging and alignment of ontologies. An example: seres3:Enable compari-
son between conceptualisations.
3. Data process related: the ontology influences a data process: for example
the creation, editing, navigation, annotation and publishing of data. These
type of purposes also can impose restrictions on the scope of the domain,
but differ from Domain Defining and Ontology process related purposes in
that the focus of the purpose lies on the data that can be represented by the
ontology instead of the conceptualisation itself. An example is EnvO3:support
the annotation of the environment of any organism or biological sample.
4. Investigative: when the ontology is created to study a system (e.g. GermanGeo1
Use as a knowledge base to study the ORAKEL system), the Ontology En-
gineering process itself (e.g. gwsg5:Investigate how to convert the German
classification schema for ecological assessment of watercourse structure into
a DOLCE-aligned domain ontology) or specific ways to formulate and encode
knowledge (e.g. gwsg4:Investigate the possible combinations of basic qualities
into complex qualities).
5. Collaboration enhancing: when the ontology should enhance social pro-
cesses. As social processes may include ontology and data related processes,
this category can overlap those two categories. For example: Pont9:Enabling
collaborative discussion of ontologies by practitioners.
6. External application: are purposes where an ontology is used by an exter-
nal application to perform a task. E.g. GIO2:Optimise information extraction
tasks in multi-media.
7. Analogous: these are purposes that refer to a similar ontology in a different
domain that is being emulated. E.g. EnvO1:Provide similar benefits as the
Gene Ontology.
7
All the purpose phrase decompositions can be found at http://spreadsheets.
google.com/pub?key=tkeezO29qTGOVZ1Rd41JbTw&gid=3
Formalisation A lightweight ontology has been created based on the shown
conceptualisation. This ontology is currently only suitable for annotating pur-
pose descriptions. In the future we would like to produce an ontology that enables
authors to easily formalise their purpose for an ontology and enables services such
as purpose comparison and classification.
3.1 Application
Currently, we are planning two extensions to this study where we investigate
whether we can apply the model of ontology purposes to compare the ontologies
in the corpus. In the first study extension we will study whether we can use the
purpose categories to determine the similarity between two ontology purposes.
This similarity can then be used to compare the ontologies. For example, our
corpus analysis shows that the NASA SWEET ontology states its purpose in
terms of ontology engineering, community benefits and domain description while
ignoring data processes such as data creation, publishing or annotations. The
Hydroseek navigation ontology has an opposite approach where the purpose
focuses on data search, mapping and navigation, while not much is said about
ontology engineering.
The second study extension will investigate whether we can find a correla-
tion between ontology metrics and ontology purpose. The intuition is that the
stated purpose of the ontology should have an impact on the design decisions
made when building the ontology, so there might be a relation between ontology
characteristics and ontology purpose. For example, an ontology that has as a
Data process related purpose (e.g. German Geo KB), may be more likely to pro-
vide instances of the modelled concepts than an ontology that does not specify
this type of purpose. Another example is the Hydroseek Navigation ontology,
that aims to make navigation tasks through data easier. This purpose may be
reflected in its small number of concepts and shallow taxonomic depth.
4 Discussion
The categorisation identified in our study suggests that it is possible to identify
generic categories to model the purpose of geographical ontologies. In fact, the
presented categorisation does not rely on geography specific terminology, which
suggest that the model could be applicable to ontologies outside of the geographic
domain. Indeed, the corpus already contains ontologies that are not strictly
geographical such as E-response(emergencies).
The dependence on the geographic domain is only apparent when we look
at the set of restrictions, where we find geographic domain restriction (e.g re-
striction for wow5 to geographic objects valuable in tourism) and data restrictions
that are strongly related to the geographical domain (e.g wow4:Allow automatic
creation of multilingual hiking path descriptions).
An unexpected result of this study is that geography-specific external pro-
cesses were not found: ontologies were built to aid information extraction in
multi-media and to study semantic reference systems, but none of the geographic
ontologies mentions specific usages in geography related tasks. The OS Hydrol-
ogy ontology has a generic data-process (use of the surveyed data by Ordnance
Survey’s customers). NASA SWEET does not mention how the data described
by the ontology will be used and only refers to the conceptualisation in terms of
ontology engineering tasks such as evolution and alignment.
A clarification for the small number of specific geography-related processes
may be the relatively small corpus of ontologies and the difficulty of finding
good purpose descriptions of the ontologies. Another clarification may follow
the argument by C.M. Keet [17] who says that many ontology authors do not
define a particular purpose for an ontology because their ultimate aim is to build
an ontology that is application independent—even when the ontology is initially
built for a particular purpose. This may be the case in the geographic domain,
when ontologies encode well-established classification schemas that already are
used by many applications (e.g. OS Hydrology ontology), so that ennumerating
the specific use cases is not desireable.
While the presented study suggests that it is possible to find generic cate-
gories of ontology purposes, the approach that we used has several limitations:
(i) the introduction of subjective interpretation of purpose descriptions; (ii) only
a subset of the geographical ontologies was used as we restricted ourselves to
ontologies that defined a single concept (i.e. we missed ontologies that define ge-
ographical features, but do not define the River concept); (iii) finding appropri-
ate purpose descriptions for ontologies was time consuming; so that, even when
a suitable ontology is found, no purpose description can be found by searching
the (semantic) web (e.g. the Mooney ontology of geographic data8 ).
5 Future Work
To address the limitations of the current study, we hope to be able to con-
tact some of the authors of these ontologies to (i) verify the ontology purpose,
(ii) get feedback on their personal goals when constructing the ontology, (iii)
validate and improve the vocabulary to describe ontology purposes and (iv) in-
vestigate whether providing the vocabulary helps to make ontology purposes
explicit, which were previously implicit and not published.
Further future work include (i) investigating whether there are links between
stated ontology purposes and features of the ontology such as number of classes
and ontology expressivity; (ii) designing and investigating the effect of tool sup-
port based on ontology purposes; (iii) researching ways to represent the ontology
purpose and the contributor’s goals to be able to model conflicts of interests to
support multi-perspective ontology development; (iv) investigate the link be-
tween ontology purpose and usage; specifically, whether we can design a shared
vocabulary to describe both purpose and usage of an ontology and whether we
can use this vocabulary to evaluate the fitness-for-purpose of an ontology.
8
http://www.ifi.uzh.ch/ddis/fileadmin/ont/nli/geography.owl
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