eScience for Sea Science: A Semantic Scientific Knowledge Infrastructure for

Marine Scientists

Kristin Stock

Allworlds Geothinking and

University of Nottingham

kristin.stock@nottingham.

ac.uk

Tim Stojanovic

University of Cardiff, UK

stojanovic@cardiff.ac.uk

Anne Robertson

EDINA, UK

a.m.robertson@ed.ac.uk

Jens Ortmann

University of Muenster,

Germany

jens_ortmann@hotmail.co

m

Mohamed Bishr

University of Muenster,

Germany

m.bishr@uni-muenster.de

Femke Reitsma

University of Canterbury,

NZ

femke.reitsma@canterbury

.ac.nz

Abstract

The COastal and Marine Perception Application for

Scientific Scholarship (COMPASS) is a knowledge

infrastructure that supports enhanced discovery of

scientific resources, including publications, data sets

and web services. It provides users with the ability to

discover resources on the basis of domain knowledge

using ontologies, and scientific knowledge, including

the scientific models, theories and methods that were

used to conduct the research described by the

resource.

The application includes an architecture that

adopts standards from the geospatial information

community to ensure interoperability between

repositories and allow interaction with content from

digital libraries. The architecture shows how

ontologies can be used as a registry for an

interoperable infrastructure.

A prototype was successfully implemented and

evaluated with users, finding enthusiasm and support

for the approach, with some suggestions for

improvements of the prototype implementation.

1. Introduction

Scientists in any discipline typically search for

scientific resources (for example, publications and data

sets) using keyword searches on the internet or digital

libraries. The COastal and Marine Perception

Application for Scientific Scholarship (COMPASS) is

a knowledge infrastructure with a web-based interface

that aims to provide enhanced tools for the discovery of

scientific resources. These tools employ semantic

information about the resources and the scientific

knowledge (for example, analysis methods, theories

and methods) that those resources encapsulate. In this

way, scientists can discover a wider range of resources

that are tailored to their requirements, and

opportunities are developed for more advanced

visualization and inference about the relationships

between scientific resources and the ways in which

science develops over time.

In addition to the goal of providing enhanced

discovery tools for scientists, the knowledge

infrastructure develops a new semantic architecture that

is based on open standards and provides interoperable

access to geospatial information provided through

standards compliant web services. Access to data sets

and scientific models is of particular importance in

geoscientific research, and the COMPASS application

was developed as a prototype with content from the

marine science domain. However, the architecture and

tools apply across all domains, with particular benefits

for those domains that use geographic information. A

number of geographic information standards are

employed.

This paper describes and evaluates the COMPASS

application. Section 2 presents related work. Section 3

describes the architecture of the knowledge

infrastructure. Section 4 describes the user interface

and explains how users interact with the knowledge

infrastructure. Section 5 evaluates the knowledge

infrastructure and presents the findings of the research.

The main contributions of this paper are firstly a

new approach to the architecture of infrastructures that

is both interoperable and semantically enriched, and

secondly a set of new approaches to the discovery of

scientific resources, using information about domain

semantics; informal user tags and the components of

scientific knowledge (including theories, analysis

methods and scientific models).

2. Related Work

Initial approaches to digital access and discovery of

scientific resources were based on print technology,

and extended the old models of library organisation,

augmenting the original paper-based searches by author

and topic with keyword searches. Search engines like

Google Scholar and online collaboratories [1] adopt

this keyword search approach.

These methods are often inadequate in searching for

relevant scientific resources, because the keywords and

title for a publication may not include information

about the scientific theories or models used, or about

many of the concepts involved. Furthermore, such

unstructured methods do not allow new forms of

information to be derived from the scientific resources,

including for example, visualisation of the use of a

scientific theory, geographic dispersion of particular

types of research and examination of temporal

relationships between research and the links between

one theory and another.

Efforts towards the creation of more effective

methods for access and discovery of scientific

resources have included the creation of models of

scientific knowledge [2] and discovery [3]. Hars’

model has been implemented as a database and web

server (cybrarium1) as a first step towards a knowledge

infrastructure [4].

Science Commons2 is another effort towards the

creation of a knowledge infrastructure, and attempts to

develop tools to facilitate the use of research data and

materials. Its focus is on issues of security, copyright

and licensing, but it also includes efforts to improve

data communication to support researchers.

1 http://cybrarium.usc.edu

2 http://sciencecommons.org/

The Geosciences Network (GEON)3 is another

example of an implementation of a knowledge

infrastructure. GEON is a project to develop a

cyberinfrastructure in support of an environment for

integrative geoscience research. The GEON provides

an infrastructure for organising four types of scientific

resources, including data, tools, web services and

knowledge. Scientists from a wide range of earth

science sub-disciplines can publish and share their

resources using GEON, in which resources can be

queried and integrated to support collaborative

research related to the earth system. Recent work has

also developed methods for using inference to examine

the links between different uses of concepts by

different scientists to support discovery [18].

A number of social networking tools to support

researchers are also available, and some of these

include literature searching capabilities. For example,

ResearchGate (www.researchgate.net) is a social

networking site for researchers that includes semantic

search capabilities. It is focussed on making and

managing contacts, but also allows members to list

their publications and provides access to publications

from the major literature databases using semantic

correlations and provides recommendations for similar

publications. It also does text matching based on

abstracts that have similar themes using semantic

methods, and is thus more advanced than keyword

searches.

While these infrastructures have varying

technological implementations, none has provided

advanced discovery, inference and visualisation

functions, and none has used ontologies to achieve this,

as is done in the knowledge infrastructure described in

this research.

3. Knowledge Infrastructure Architecture

The knowledge infrastructure uses an architecture

that is based on open standards, particularly focusing

on those from the geospatial standards community,

ensuring that geospatial resources can be supported by

the knowledge infrastructure.

3.1. The software architecture

Geospatial registry standards describe an interface

through which systems can interact with all of the

metadata and related information that is needed to

manage a knowledge infrastructure. Such information

includes descriptions of the resources and where they

3 http://www.geongrid.org/

can be found on the internet. The use of a standard for

the definition of such an interface ensures

interoperability between infrastructures, as a common

format for communication can be used.

Existing registry standards have limited ability to

represent the semantics (or meaning) of the resources

in the infrastructure. Such semantics may be

represented in a number of ways, the most common

current approach being the use of description logics

like Web Ontology Language (OWL). This language is

used to develop ontologies that describe the semantics

of a domain, and web service ontologies (for example,

OWL-S ontologies) are used to describe the semantics

of web services in terms of the kinds of processing they

do and the information they provide. Thus in the

marine domain, ontologies may be developed to

describe marine concepts (for example, waves), and

web service ontologies may be developed to describe

marines services (for example, a web service that

provides wave modeling).

The benefits of adding semantic content to registries

has been recognized and a number of efforts have been

made to work towards semantic registries, including the

mapping of OWL constructs to ebRIM registries [5];

the addition of DAML-S (another ontology language)

web service ontologies to UDDI registries [6]; the

transformation and storage of OWL ontologies in

ebRIM registries [10] and the representation of

semantics within registries using Feature Type

Catalogues [11]. Other work has used ontologies to

assist with discovery of resources, but not in the

registry itself [7, 8, 9, 17].

However, none of these efforts have stored

ontologies in the registry in their native form in a way

that allows existing ontology management and

inference tools to be used without extraction and

transformation or duplication of content. The

architecture of the knowledge infrastructure uses a new

approach: the ontology-registry. In order to create this,

an OWL application profile for CSW (the Open

Geospatial Consortium registry standard that was used

for this work [12]) was developed to show how OWL

ontologies could be stored in a standards-compliant

registry and to define the way in which the interface

could be adapted to meet the requirements of

ontologies [13].

This new application profile may thus be used to

allow registries to store semantic content without the

need for duplication or transformation, and also to

ensure interoperability between different

infrastructures.

Figure 1. COMPASS Software Architecture

Figure 1 shows the architecture of the knowledge

infrastructure, including the ontology-registry.

This architecture illustrates the ontology registry,

the CSW interface being the registry standard interface,

which accesses the ontologies through some knowledge

management middleware (in the COMPASS prototype,

Jena was used).

The architecture also illustrates interoperability with

digital library repositories. Such repositories have

large volumes of data about scientific resources, but

typically do not include semantic information. Thus

the digital library repositories could be accessed

through the CSW interface, but the search capabilities

were limited.

The top row of the architecture diagram shows the

user interface. The discovery interface (see Section 4)

allows users to search for scientific resources, taking a

user selection and formulating the CSW request (using

the standards-based OWL application profile for

CSW). Other systems could potentially also access the

CSW interface in this way, allowing system-to-system

interaction. The architecture also includes a user

interface for user tagging, which allows users to add

their own informal tags to resources (in addition to the

formal semantics that are expressed through the

ontologies). New ontologies may also be created and

edited using ontology management software.

3.2. The information architecture

The ontology-registry stores all content in the form

of ontologies. This decision was made to avoid the

need for two separate storage structures, and

considering the need for semantic information about a

significant proportion of the content of the ontology-

registry. A number of different ontologies are used, as

depicted in Figure 2.

Figure 2. COMPASS Information Architecture

The most important of these ontologies are:

• domain ontologies describe the semantics of the

concepts in the domains that the scientific resources

address (for example, concepts in the marine

domain);

• the scientific knowledge ontology describes the

semantics of scientific knowledge, and the theories,

methods and models used in science;

• the information source ontology describes the

semantics of the scientific resources (for example,

metadata);

• the tag ontology contains informal user tags that are

applied to the resources;

• application ontologies describe each scientific

resource (except web services) using concepts from

the domain, scientific knowledge, information

source and location ontologies and

• web service ontologies describe each web service (a

type of scientific resource) using concepts from the

domain and scientific knowledge ontologies, as well

as its own concepts.

4. User Interface

The second major contribution of this research is a

set of new approaches to the discovery of scientific

resources, as shown in the Figure 3.

The COMPASS discovery interface (Figure 3)

includes a series of boxes that allow a query to be built

using a number of different approaches to scientific

discovery. The currently selected query is previewed

in the top right box.

Figure 3. COMPASS Discovery Interface

4.1. Discovery by domain ontology

The top left box allows users to specify a search on

the basis of domain concepts. This box displays the

domain ontology or ontologies visually, showing

concepts from the marine domain (in the case of the

prototype), but extendable to any domain. The

ontology may be viewed in its entirety, or sub-areas can

be examined in detail. All of the properties linking

concepts in the ontology are displayed as lines (colour

coded by property type). Users can select concepts and

also perform semantic relaxation, meaning that they

can specify that additional concepts be included that

are generalizations, specializations or linked in other

ways to the initially selected concept. For the purposes

of the prototype, a domain ontology of marine

instruments was developed, and users could select

particular types of instruments or general terms of

interest in the marine domain.

If the user selects particular domain ontology

concepts, only those resources that include those

concepts in their application ontology (in the case of

publications or data sets) or their web service ontology

(in the case of web services) are returned.

4.2. Discovery by user tags

The middle left box allows users to select informal

tags that have previously been applied by users in

another part of the interface (see Section 4.6). This is

done simply by clicking on the tag.

If the user selects particular user tags, only those

resources that have those tags applied to them are

returned.

4.3. Discovery by scientific knowledge ontology

The bottom left box allows users to select particular

scientific elements. The scientific knowledge ontology

itself [14] provides a framework of scientific

knowledge and includes elements like scientific

theories, models, analysis and sampling methods,

inductions, deductions and facts. The application

ontology for each resource creates or references

instances of those elements that describe the scientific

content of the resource. For example, a publication

that uses principal components analysis might create or

reference an instance of the Analysis element in its

application ontology. Similarly a web service that

provides numerical tidal modeling might create or

reference an instance of the ScienceModel element in

its web service ontology.

Users may thus select the scientific instances that

are of interest to them in identifying scientific

resources, and only those resources are returned. It is

also possible to perform semantic relaxation on the

scientific knowledge ontology if the user is also

interested in related scientific elements.

4.4. Discovery by time and space

Users may also specify a limitation in the time or

location of the scientific resource. The time refers to

the date of publication or data collection or processing

(in the case of a web service or data set). All resources

have a bounding box attached to them indicating the

location of the research. In the case of data sets, this is

usually where the data was collected, while in the case

of publications and models published as web services,

it may relate to the area over which the research

applies. The prototype included resources from the

Severn and Forth Estuaries in the UK, but some

scientific publications proposed theories that applied

across wider geographical regions.

4.5. Combined Discovery

The combination of these different approaches to

querying allows users to build up various selections to

identify different groups of scientific resources. For

example in the marine domain:

• if a user is interested in the way a particular

analysis method has been used in different

disciplines, she can select the appropriate concepts

in the scientific knowledge ontology, but not

restrict the domain;

• if a user is interested in applications of a particular

scientific model only in Europe, he can select the

appropriate concept from the scientific knowledge

ontology and the restrict the map location;

• if a user is interested in research using a particular

group of marine instruments, she can select the

appropriate concept and then use semantic

relaxation to include all specializations of that

group of instruments;

• if a user is looking for a web service that runs a

particular scientific model based on a particular

theory of the ways in which waves affect

coastlines, she can select the appropriate scientific

model concept in the scientific knowledge

ontology and the appropriate concepts for waves

(etc) in the domain ontology.

4.6. Results

After the user has selected all of the items of interest

using the multiple boxes in the discovery interface, she

is presented with the resources that fulfill the

selections, as shown in Figure 4. Users are also

provided with recommendations based on geography

(similar location), theme (connected to semantically

similar domain ontology concepts) or scientific

knowledge (connected to semantically similar scientific

knowledge elements). Users may also apply their own

tags to the resources, and these then appear in the

discovery page.

Figure 4. COMPASS Results Page

The user may view metadata about the resource (in

the right box), and may also access the actual resource

itself. In the case of publications, the link to the

resource accesses a copy of the resource on the internet

(or the web site of the owner of the publication if it is

not publicly accessible). In the case of data sets, the

user may download the data set. In the case of web

services, the user may execute the web service.

The prototype supports web services that use the

standards of the Open Geospatial Consortium, namely

Web Feature Service [15] and Web Map Service [16].

These web services are described using web service

ontologies that contain the URL and parameters

required to execute the service, and also any additional

information that is required from the user to execute the

web service. This information is stored in the web

service ontology and the dialog box that prompts the

user for input is generated on-demand on the basis of

the content of the web service ontology. The user may

then select the parameters (for example, the map layers

that are of interest) and execute the web service.

5. Evaluation

A prototype of the COMPASS knowledge

infrastructure was created using a small sub-domain

from marine science. This Section describes the main

outcomes of the evaluation of the approach described

in this paper.

5.1. The user experience

An evaluation of the user interface was conducted

with 12 marine scientists. The scientists were allowed

to use all of the functionality of the interface, and were

particularly asked to consider the different approaches

to resource discovery: by domain ontology, by user

tags and by scientific knowledge ontology.

Discovery by user tagging received the most

positive response, as users found it simple, easy to use

and easy to understand.

Discovery by domain ontology was recognized as

powerful and innovative, but there was some criticism

of the clutter and complication involved.

Discovery by scientific knowledge ontology was not

well understood. Although users still liked the idea in

principle, they found it difficult to understand and use.

This may be partly because it represents a completely

different approach to resource discovery than anything

considered before (in contrast to the domain ontology,

which although innovative is an extension of the notion

of keyword searching with the imposition of some

structure). The appropriateness of this way of

scientific resource discovery may also be related to the

types of science that individuals are involved with, and

whether they are accustomed to thinking in terms of

scientific knowledge as structured in the scientific

knowledge ontology. While some scientists regularly

work with and discuss theories, models, hypotheses and

other aspects of the scientific process, other scientists

adopt a more pragmatic approach and thus these

concepts may be less meaningful to them.

When asked to identify aspects of the interface that

they particularly liked, users identified the visual nature

of the interface; the ability to view and explore

concepts and their interrelationships; the ability to

select by geographical region; the discovery by user

tags and the ability to build up a query in steps.

The majority of the aspects that users didn’t like or

suggestions for improvement were based on the

limitations of the prototype implementation rather than

the concept, which was supported enthusiastically. The

prototype had performance and reliability issues that

affected the user experience, and users said that a Help

function was needed. Both of these issues can be

solved in future development of the knowledge

infrastructure. It is also expected that additional

explanations about the scientific knowledge ontology

would assist users in realizing the potential of the

function.

5.2. Data population

As described in Section 3.2, the knowledge

infrastructure is supported by a number of ontologies

and some work is required to build these ontologies.

This is one of the shortcomings of the approach, in that

ontology creation can be time consuming. The

ontology building activities that required the most

effort related to the domain ontology, the application

ontologies and the web service ontologies. The other

ontologies were only required to be created once and

could then be used for the entire knowledge

infrastructure, so are less of an issue.

The domain ontology was created as part of the

project using workshops with domain experts, followed

by significant effort by ontology engineers. Only a

small part of the marine domain was modeled in the

ontologies: that describing a particular set of marine

instruments. One problem is that in order for the

knowledge infrastructure to be fully supported, domain

ontologies with a large scope are required to allow

users to select both detailed concepts from a particular

specialized domain and also more general concepts

across multiple domains. For example, in the

prototype, publications were selected about particular

marine instruments that appeared in the marine

instruments ontology that was developed for the

project, but usually these publications also related to

general concepts from the marine or wider domains,

like environmental impact assessment, or biodiversity.

Thus a full operational knowledge infrastructure

requires a comprehensive ontology across a wide range

of domains. On the other hand, some efforts to create

such ontologies for the geosciences have been

undertaken (for example, SWEET4), and the

architecture supports the inclusion of multiple domain

ontologies, so as new ontologies are created by other

projects and groups, these can easily be added to the

COMPASS knowledge infrastructure.

The knowledge infrastructure depends on the

existence of an application ontology for each

publication and data set and a web service ontology for

each web service. The creation of these ontologies

requires some manual effort. The application

ontologies and web service ontologies include standard

metadata about the resource, and this component could

potentially be automatically populated from existing

metadata (for example digital library) sources.

However, the application ontologies and web service

ontologies also include concepts from the domain

ontology (to indicate the subject area of the resource)

and the scientific knowledge ontology, to describe the

scientific aspects of the resource. This part cannot

easily be automated, and requires manual effort. The

scientific knowledge aspect is particularly time

consuming because it requires the resource and the

process by which it was created to be well understood.

The most practical solution to this problem would be to

have the creators of scientific resources create

application ontologies for them when they are first

published, if incentives could be created to do this.

5.3. Architecture

The architecture of the knowledge infrastructure

worked well and achieved the broad goals of a

standards-based, semantically-enriched yet

interoperable registry that was able to interoperate with

other repositories (for example, digital libraries).

There were some challenges involved in fitting the

ontology model into the Open Geospatial Consortium

catalogue standard (CSW) [12], mainly because that

standard is based around a notion of database records

that have particular attributes that can be retrieved as a

self-contained record. However, ontologies do not

have a clearly fragmented structure like this, and it is

sometimes difficult to retrieve subsets of data. This

sometimes resulted in the retrieval of more content than

was strictly required, with performance implications.

Another finding of the work was that the use of

OWL-S for web service ontologies was cumbersome.

Definition of a web service using OWL-S was time-

consuming and involved significant duplication (for

4 http://sweet.jpl.nasa.gov/ontology/

example of inputs and outputs). Although this

duplication resulted from OWL-S design decisions that

were justifiable, the practical application of OWL-S for

this project required a number of modifications to make

it feasible to use.

The web services used in the project all conformed

to Open Geospatial Consortium (GOC) web service

specifications, and these were defined in OWL-S for

the project, and then instances of specific web services

were created using the generic OGC web service

specification ontologies.

Modifications that were made to model the web

services in OWL-S for the project included defining

inputs and outputs in only one place and referencing

them; defining a model for grounding that was suitable

for representation in OWL (rather than WSDL) and

that conformed to OGC standards and creating an

extenstion to OWL- to allow users to select parameters

for on-demand execution of web services. This

extension defined the details of the parameters for

which users should be prompted and what to do with

the result. The software that accessed and used the

OWL-S web service ontologies had to be aware of

these changes in order to use the content correctly.

6. Conclusions

The COMPASS knowledge infrastructure presents a

new approach to the discovery of scientific resources

on the basis of domain knowledge with its own internal

structure and inter-relationships, and scientific

knowledge, including theories, models and scientific

methods. Furthermore, the knowledge infrastructure

adopts a standards-based architecture that is

interoperable and illustrates how a registry conforming

to geospatial information standards can also be

semantically enriched in a way that allows ontologies

to be stored efficiently and without duplication, but still

supports the registry standards that permit

interoperability.

The approach was supported by users

enthusiastically, although they found the prototype

implementation required some improvements and

problems of understanding and clutter presented some

barriers to usability. The knowledge infrastructure

architecture was valid, but a significant overhead in

creating ontologies to support the infrastructure

necessitates:

• the future development of tools to support

automatic population of metadata elements of

application and web service ontologies;

• the adoption of existing and still to be developed

domain ontologies from other projects and

• the involvement of scientists in the process of

describing the semantics of their own resources,

particularly with reference to the science aspects,

and the need for tools to support this and make it

easy and convenient for scientists.

The work presented in this paper illustrates how

knowledge infrastructures can be used to enhance the

process of scientific investigation and the development

of new ideas. Furthermore, future work may make use

of the scientific knowledge aspects of the knowledge

infrastructure by developing tools and approaches for

visualization and inference of patterns of scientific

knowledge across resources, leading to new potential in

the discovery of patterns in the development of science,

identification of work being done in different domains

using similar approaches and cross-fertilization

between disciplines. These extended uses of the

knowledge infrastructure further illustrate its exciting

potential and long term benefits for the advancement of

science.

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