CHARACTERIZING SEMANTIC SERVICE PARAMETERS

WITH ROLE CONCEPTS TO INFER DOMAIN-SPECIFIC

KNOWLEDGE AT RUNTIME

Alban Gaignard, Johan Montagnat

CNRS / UNS, I3S lab, MODALIS team, Sophia-Antipolis, France

Bacem Wali, Bernard Gibaud

INSERM / INRIA / CNRS / Univ. Rennes 1, IRISA Unit VISAGES U746, Rennes, France

Keywords:

Semantic web services, Role modeling, Reusable inference rules, Scientiﬁc workﬂows.

Abstract:

E-Science platforms leverage Service Oriented Architecture (SOA) principles to deliver large catalogs of data

processing services and experiments description workﬂows. In spite of their growing success, the usability of

these platforms is hampered by their catalogs size and the domain-speciﬁc knowledge needed to manipulate

the services provided. Relying on domain ontologies and semantic services to enhance the understanding and

usability of e-Science platforms, our contribution is twofold. First, we propose to delineate role concepts

from natural concepts at domain ontology design time which leads to a neuroimaging role taxonomy, making

explicit how neuroimaging datasets are related to the data analysis services. Then we propose to exploit,

at workﬂow runtime, provenance information extended with these domain roles, to infer new meaningful

semantic annotations. Platform semantic repositories are thus transparently populated, with newly inferred

annotations, through the execution of e-Science workﬂows. A concrete example in the area of neurosciences

illustrates the use of role concepts to create reusable inference rules.

1 INTRODUCTION

Semantically representing information has become a

de facto technique to enrich e-Science experimental

platforms with domain-speciﬁc knowledge. This ap-

proach aims at facilitating platforms usage, sharing of

experimental data and results, and experiments them-

selves, to ﬁnally foster collaborations among large

user groups. Conceptualizing domain knowledge, on-

tologies became a cornerstone for the underlying In-

formation Systems, as they are built upon controlled

vocabularies, logical constraints and inference rules.

Generally relying on Service Oriented Architec-

tures (SOA), e-Science experimental platforms pro-

vide tools dedicated to the publication, the identiﬁ-

cation, and the invocation of data processing services.

However the technical description of services (e.g. us-

ing WSDL) does not provide any understanding on

the nature of the information processed nor on the op-

erations applied. Exploiting catalogues of data proce-

ssing services, e.g. to design ﬂows of services (work-

ﬂows) requires a clear understanding of how data is

processed and the nature of the data transformation

implemented by the services. Today, users are ex-

pected to have acquired this knowledge, which limits

the platforms usability to a restricted number of ex-

perts.

In this context, and relying on ontologies, seman-

tic (web) services tend to explicit the understanding of

(i) the nature of processed data and (ii) the nature of

the information processing applied to beneﬁt, both at

experiment design-time and runtime, from the knowl-

edge on the services manipulated. Different levels of

semantic information can be distinguished:

1. Generic information, related to the technical de-

scription of services (e.g. semantic service de-

scriptions based on OWL-S) or related to the ser-

vice invocation which can later be used to produce

provenance traces (e.g. following the Open Prove-

nance Model).

Gaignard A., Montagnat J., Wali B. and Gibaud B..

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME.

DOI: 10.5220/0003648200590070

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 59-70

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2. Domain-speciﬁc information related to the nature

of the information processing realized by a service

invocation and the nature of the data manipulated

(e.g. taxonomies describing the nature of Dataset

and Dataset-processing). This knowledge can be

used to validate service invocations, by ensuring

that the expected types of data are used when in-

voking a service.

3. Domain-speciﬁc information related to the Role

played by the data involved in the service execu-

tion, from the service point of view. This knowl-

edge is needed both to ensure coherency of service

invocations, and to reason on the service invoca-

tion effect on the data processed.

Leveraging existing ontologies to describe generic

information as well as domain-speciﬁc nature of data

and processing tools, this paper focuses on the third

level of semantic information. The proposed ap-

proach tackles 3 aspects of semantic services manip-

ulated:

• It clariﬁes the bindings between service descrip-

tions and domain concepts through a taxonomy of

domain-speciﬁc Roles.

• It enables the coherency of service workﬂows de-

sign.

• It makes it possible to infer new knowledge along

platform exploitation. This last point is achieved

by describing reusable domain-speciﬁc knowl-

edge inference rules associated to speciﬁc na-

tures of processing. The application of such rules

on a semantic database containing traces of ser-

vices invocation enriches the experimental plat-

forms with new valuable expert information.

We rely on the NeuroLOG platform (Montagnat

et al., 2008) to implement the concepts and support

experiments reported in this work. NeuroLOG is a

distributed environment designed to support the setup

of multi-centric studies in neurosciences. The On-

toNeuroLOG ontology (Temal et al., 2008) was de-

signed in the context of the platform development to

enhance the sharing of neuroimaging data and associ-

ated data analysis services.

The remainder of this paper is organized as fol-

lows. Section 2 presents some background on role

modeling and how it could be related to existing ini-

tiatives in the Semantic Web Services area. Section 3

motivates our approach through a small neuroimaging

workﬂow example. Ontologies supporting our work

are brieﬂy presented in section 4. The beneﬁts of re-

lying on Role concepts when designing a domain on-

tology are exposed in section 5, followed with sec-

tion 6 brieﬂy illustrating how we complemented our

workﬂow environment with semantic web technolo-

gies. We ﬁnally discuss and conclude our approach in

section 7.

2 BACKGROUND INFORMATION

2.1 Role Modeling

In conceptual modeling, it is now agreed to sepa-

rate several categories of concepts, for instance those

characterizing the nature of an entity from those char-

acterizing their relations to each others. Henriksson

et al. propose a methodology based on the design of

role-based ontologies, extending standard ontologies,

to enhance ontology modularization and reusability.

They promote a clear delineation between Natural

Types and Role Types (Henriksson et al., 2008) : “In

role modeling, concepts that can stand on their own

are called natural types, while dependent concepts

are called role types”. Sowa (Sowa, 1984) ﬁrst in-

troduced Natural Types to describe what is essential

to the identity of an individual, and Role Types to de-

scribe temporal or accidental relations to other indi-

viduals. The methodology proposed by Henriksson et

al. consists in (i) identifying the natural types of the

domain, (ii) identifying accidental or temporary rela-

tionships between individuals and ensuring that role

models are self-contained (for reusability) and ﬁnally

(iii) deﬁning bridge axioms to bind role types to natu-

ral types (or to link individuals through properties de-

ﬁned in the role model). This approach is particularly

interesting in our context since in Life Science ontolo-

gies, the design effort generally focuses on the ﬁrst

step. Moreover, e-Science experimental platforms are

generally data-driven and well supported by ontolo-

gies describing the nature of data. But few efforts

concentrate in making explicit the knowledge relat-

ing data to their analysis services more deeply than

just using information on data nature.

2.2 Web Service Ontologies

Semantically enhanced e-Science experimental plat-

forms usually rely on standard generic service ontolo-

gies to describe data analysis services. The following

paragraphs brieﬂy describe major service ontologies

and how they consider relations between data and ser-

vices.

The OWL-S Proﬁle ontology (Martin et al., 2007),

one of the three ontologies forming the OWL-S pro-

posal, aims at describing what the annotated service

does. The service Proﬁle presents a high-level inter-

face of the service through the properties hasParam-

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

eter, hasInput, hasOuput. These properties link the

OWL-S Proﬁle ontology to the OWL-S Process ontol-

ogy (aimed at describing the service internal behav-

ior) which deﬁnes the service parameters (Parameter

class) and their subclasses (Input and Output classes).

The type of parameters is given through the param-

eterType property of the Process ontology and spec-

iﬁes the classes/datatypes the value of the parameter

belongs to. According to the OWL-S speciﬁcation,

nothing is said regarding how these parameter val-

ues are related to the service process and as a con-

sequence, these types should be considered as natu-

ral types as they are deﬁned by Sowa (Sowa, 1984).

To specify the relationship of parameter values to the

process, it should be beneﬁcial to rely, through the

parameterType property, on a role ontology designed

according to the methodology proposed by Henriks-

son et al..

FLOWS (Gruninger et al., 2008; Battle et al.,

2005) speciﬁes a ﬁrst-order logic ontology for Web

Services. It aims at enabling reasoning on the seman-

tics of services and their interactions. FLOWS has

largely been inﬂuenced by OWL-S but in addition,

it addresses interactions with business process indus-

try standards such as BPEL. FLOWS differs from

OWL-S by properly handling messages as core con-

cepts. Messages are deﬁned in FLOWS by a mes-

sage type, characterizing the type of the content, and

a payload, the content itself. FLOWS deﬁnes also

three relations to relate atomic process invocations

to messages they consume as input or they produce

as output: produces, reads, and destroy message.

The relations are very generic and do not charac-

terize more precisely the consumption/production of

messages through domain-speciﬁc entities. However,

FLOWS proposes the described by relation to asso-

ciate a ﬂuent to a message. Fluents are used to model

“changing” parts of the world. The described by rela-

tion aims at providing information on how the content

of the message impact the service invocation while

consuming/producing it. Intuitively, since role types

are deﬁned by Sowa as accidental (or evolving during

time) relationships between entities, FLOWS’s ﬂuents

could be a way to model how data are interpreted by

analysis services through Roles.

WSMO (Roman et al., 2006) is based on Orches-

tration to describe the internal behavior of services,

and on Choreography to describe their external be-

haviors. The Choreography of a service is described

through the importation of a domain ontology, which

deﬁnes the choreography state signature. This sig-

nature speciﬁes, among other things, the service in-

puts and outputs as instances of the imported ontol-

ogy. WSMO is a rich service modeling and enacting

framework but it does not cover precisely the charac-

terization of how processed or produced data are re-

lated to services in terms of roles. Relying on external

ontologies, WSMO service interface remains compat-

ible with any domain ontology designed using a clear

separation between natural types and role types.

SAWSDL (Kopeck

y et al., 2007) is the W3C rec-

ommendation to semantically annotate WSDL and

XML Schema documents specifying standard Web

Services. These documents are bound to semantic en-

tities through the modelReference XML attribute. The

value assigned to a modelReference comprises a set

of zero or more URIs identifying concepts in an on-

tology. Again, this speciﬁcation does not bring any-

thing new to separate the natural type of the annotated

WSDL message from how it is related to the Web

Service (its role type). However, depending on the

availability of an ontology of roles, modelReference

attributes could be used to bind role types to service

parameters.

Originating from the WSMO initiative, WSMO-

Lite (Vitvar et al., 2008) is built upon SAWSDL and

is a lightweight bottom-up approach, to semantically

describe Web Services and to enable reasoning on (i)

their associated semantic annotations, and (ii) their

interactions. Since WSMO-Lite uses SAWSDL to

bridge domain-speciﬁc ontologies with the service

description, roles types should be considered as an ex-

ternal feature, coming from the design of the domain

ontology.

2.3 Semantic Workﬂow Environments

Being based on either standard service ontologies, or

home-made approaches, the following paragraphs de-

scribe initiatives aiming at enhancing service discov-

ery, in the context of workﬂow environments.

The METEOR-S (Sheth et al., 2008) research

project is a major initiative in the Semantic Web Ser-

vices area. The approach is based on a peer-to-peer

middleware to address service discovery and publi-

cation. SAWSDL is used for both services annota-

tion (through modelReferences) and data mediation

(through schema lifting/lowering).

Built upon the

Grid ontology (Wolstencroft

et al., 2007), a bioinformatics service and domain

ontology, FETA (Lord et al., 2005) is a service dis-

covery framework characterized by a light-weight se-

mantic support and a semi-automatic approach. Three

main actors are distinguished in this framework: both

knowledge engineers and service annotators provide

semantic enhanced web services consumed by scien-

tists. Also built upon the

Grid ontology, the BioCat-

alogue (Bhagat et al., 2010) initiative is a community-

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER

DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME

driven, and curated service registry aiming at guiding

users into a jungle of web services through the regis-

tration and annotation of web services and the brows-

ing of resulting annotated web services. Several kinds

of annotations are available going from free text, to

tags or ontology terms. BioCatalogue allows, among

other kind of annotations to operationally (e.g. infras-

tructure, runtime constraints) or functionally describe

a service. Functional annotation covers information

related to what the service does, but also its function

and the format of input or output data. The function

annotation of data with regard to a given web service

seems to be close to Role types previously introduced

but few information is available to precisely describe

this kind of annotation.

The BioMOBY project aims at providing interop-

erability for biological data centers and analysis cen-

ters. SAWSDL has been used in this context and this

real-world application is one of the few existing ini-

tiatives (Gordon and Sensen, 2008). The focus is on

interoperability and therefore on schema mapping an-

notations of SAWSDL, implemented through XSLT

stylesheets. The entry-point is a SAWSDL Proxy

servlet, in front of a web service provider, a semantic

registry, and a schema mapping server. As a continua-

tion of this initiative, the SADI project (Withers et al.,

2010) proposes guidelines and best-practices to en-

hance semantic service discovery at workﬂow design

time. Semantic services are indexed in the catalog

through the new set of RDF properties describing the

resulting new semantic features associated to input

data. The service discovery is based on searches over

data properties consumed as input and over the pro-

duced new properties. This approach also aims at re-

ducing ambiguity of search queries through more pre-

cise properties, describing the relationships between

input and output data. We propose to address such re-

lationships at domain ontology design time, through

a taxonomy of Roles, clearly identifying the role of

data with regard to their analysis services.

3 MOTIVATING USE CASE

3.1 Image Registration Workﬂow

The workﬂow illustrated in Figure 1 represents a typi-

cal image registration process commonly encountered

in neurosciences workﬂows. It consists in superim-

posing two medical images acquired independently

into the same coordinate system. The sample regis-

tration process is decomposed into two steps. First,

the registration itself consists in calculating, from the

input brain MRI and a brain atlas, a geometrical trans-

Registration Re-sampling

100

MRI

Matrix

Figure 1: A typical neuroimaging workﬂow mixing several

nature of data and processing.

formation expressed by a transformation matrix. Sec-

ond, the resampling step effectively aligns the input

brain MRI by applying the transformation expressed

through the registration matrix.

In spite of its apparent simplicity, this workﬂow is

interesting for the following reasons. First, this work-

ﬂow mixes two services of different nature, whose

meaning has been agreed upon within the image pro-

cessing community. In other words, the knowledge

about what kind of underlying treatment is clear for

the community and is generally not explicit at the

tooling level. Second, this workﬂow consumes and

produces data of several natures (medical images,

transformation matrix) expressed through raw ﬁles at

the tooling level. Again, these ﬁles have a precise

meaning from the user community point of view, with

regard to their content. Finally, the ﬁrst step of the

workﬂow takes two ﬁles as input, sharing the same

nature, both are brain MRIs, but they play different

roles from the processing tool perspective. The ﬁrst

one is used as the reference image for the registration

process (atlas) whereas the second one is used as the

ﬂoating (moving) image. This knowledge is hidden

at the tooling level, and even for domain experts, the

variability of tools makes their conﬁguration not triv-

ial.

3.2 Enriching the Semantic Repository

with Valuable Annotations

Relying on a semantic data repository together with a

reasoning engine, we consider in this paper a method-

ology for producing and deducing new meaningful

facts from the user community perspective. For ex-

ample, considering the result of the registration work-

ﬂow presented in Figure 1, it should be interesting to

retrieve, the atlas used in the registration. More gen-

erally, our approach tends towards the propagation of

the effect of a service (or a sub-part of the workﬂow)

to the produced data. For instance, we would like

to automate the generation of a fact saying that “this

dataset can be superimposed with this dataset”, be-

cause some processing tools might require that their

inputs have beforehand been registered.

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

Registration Re-sampling

100

wasGeneratedBy

used

wasGeneratedBy

can-be-superimposed-with

Figure 2: Linking data and processes through generic and

domain-speciﬁc relations.

Figure 2 illustrates the semantic relations estab-

lished between entities involved in the sample work-

ﬂow, i.e. data and services. Black arrows are rela-

tions that can be created on-the-ﬂy during each ser-

vice invocation. It states data production and con-

sumption knowledge. Beyond linking together data

and processes (i.e. capturing provenance informa-

tion), we want to rely on both an ontology and a

reasoning engine to infer relevant domain informa-

tion using rules expressing domain knowledge. For

instance the dashed arrow represents such informa-

tion derived using a domain-speciﬁc rule embedding

domain knowledge about the overall registration pro-

cess.

As it will be shown in Section 5, this kind of

knowledge inference is possible if the services seman-

tic description is rich enough to properly deﬁne the

Roles of processed data in the context of services in-

vocation. In addition, such high-level semantic de-

scription can be used to validate the coherency of

ﬂows of services. Before entering into the details, the

next Section describes the ontologies on which this

work is grounded.

4 SUPPORTING ONTOLOGIES

4.1 Domain Ontology

The OntoNeuroLOG ontology was developed to

provide common semantics for information sharing

throughout the NeuroLOG system. Indeed, the ulti-

mate goal of NeuroLOG was to allow the success-

ful sharing of neuroimaging resources provided by

collaborating actors in the ﬁeld of neuroimaging re-

search, the term resources covering both neuroimag-

ing data (such as images) as well as image processing

programs, shared as services. This ontology is used as

a reference to query and retrieve heterogeneous data,

thanks to the mediation system, as well as to annotate

consistently the shared services, i.e. denote what sort

of processing such services actually achieve and what

data they accept as input and produce as result.

OntoNeuroLOG was designed as a multi-layer ap-

plication ontology, relying on a number of core on-

tologies modeling entities that are common to several

domains. The whole ontology relies on DOLCE (De-

scriptive Ontology for Language and Cognitive Engi-

neering), a foundational ontology that provides both

the basic entities (at the top of the entities’ taxonomy)

and a common philosophical framework underlying

the whole conceptualization. The ontology was de-

signed according to the OntoSpec methodology (Kas-

sel, 2005), which focuses on the writing of semi-

formal documents capturing rich semantics. This is

followed by an implementation of a subset of the on-

tology in OWL, the web ontology language. The

deﬁnition of this subset and the choice of the rele-

vant OWL dialect take into account the speciﬁc needs

of the application. Two subsets of OntoNeuroLOG

were used in the context of this work, the ontology of

Dataset and the ontology of Dataset processing, in-

troduced hereafter.

4.1.1 Dataset Sub-ontology

Datasets are Propositions (i.e. Non physical en-

durants) that represent the content of data ﬁles used

in neuroimaging. The taxonomy of Datasets is orga-

nized according to several semantic axes. The ﬁrst

denotes what facet of the subject is explored, e.g.

Anatomical datasets explore the subject’s anatomy

whereas Metabolic datasets explore brain metabolic

processes. The second axis classiﬁes Datasets ac-

cording to some imaging modality, such as Com-

puted Tomography (CT), Magnetic resonance (MR),

Positron emission tomography (PET). This axis in-

cludes the numerous sub-modalities met, e.g., in MR

imaging such as T1-weighted MR dataset, Diffusion-

weighted MR dataset, etc. The third axis focuses

on Datasets that result from some kind of post-

processing, such as Reconstructed datasets, Registra-

tion datasets, Segmentation datasets, etc.

Datasets may bear properties of Representational

objects (since Propositions are Representational ob-

jects), such as ’refers to’, which denotes the ability

to refer to any kind of Particular. This property can

be used to refer, e.g. to the Subject (i.e. the patient)

concerned by a particular Dataset. For instance, a

property called ’can be superimposed with’ was in-

troduced to relate two Datasets that can be superim-

posed with each other, such as a Segmentation dataset

(i.e. an object mask obtained through a segmentation

procedure) and the original dataset from which it was

obtained.

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER

DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME

4.1.2 Dataset-processing Sub-ontology

Dataset processings are Conceptual actions (i.e. Per-

durants) that affect Datasets. The taxonomy of

Dataset processings covers the major classes of image

processing met in neuroimaging, such as: restoration,

segmentation, ﬁltering, registration, re-sampling, etc.

Axioms attached to each Dataset processing class

usually denote which classes of Datasets are being

processed or result of the corresponding processing.

For example, a Reconstruction ’has for data’ some

Non-reconstructed dataset and ’has for result’ some

Reconstructed dataset; a Segmentation ’has for re-

sult’ some Segmentation dataset.

4.2 Ontology of Web Services

In addition, an ontology was deﬁned to describe Web

Services grounded to the DOLCE foundational con-

cepts. It introduces the notions that are classically in-

volved in WS speciﬁcations such as the notions of in-

terface (ws-interface), operation (ws-operation), ser-

vice inputs and outputs (input/output-variable). Be-

sides, the model introduces a ’refers to’property to es-

tablish relationships with the classes of data process-

ing that a particular ws-operation implements (such

as rigid-registration or segmentation), as well as with

the classes (natural types) of entity that the input and

output variable actually represent.

4.3 OPM Ontology

The Open Provenance Model (Moreau et al., 2011)

initiative (OPM) aims at homogenizing the expres-

sion of provenance information on the wealth of data

produced by e-Science applications. Among other

things, OPM enables the exchange of provenance in-

formation between several workﬂow environments. It

eases the development of tools to process such prove-

nance information, and ﬁnally facilitates the repro-

ducibility of e-Science experiments.

OPM is materialized through a natural language

speciﬁcation and three formal speciﬁcations: an XML

schema (OPMX), an OWL ontology (OPMO) and a

controlled vocabulary, with simpler OWL constructs

(OPMV). OPM deﬁnes directed graphs representing

causal dependencies between “things”. A Causal de-

pendency is deﬁned as a directed relationship between

an effect (the source of the edge) and a cause (the des-

tination of the edge). The nodes of the provenance

graph might be either an Artifact (immutable, state-

less element), or a Process (actions performed on an

Artifact and producing new ones), or an Agent (en-

tity controlling or affecting the execution of a Pro-

cess). The edges of the graph represent (i) dependen-

cies between two artifacts (wasDerivedFrom) to track

the genealogy of artifacts, (ii) dependencies between

two processes (wasTriggeredBy) to track the sequence

of processes, and (iii) dependencies between artifacts

and processes (used/wasGeneratedBy) to track the

consumption and the production of artifacts through

processes. Additionally OPM allows to track the links

between processes and their enactor agents through

wasControlledBy dependencies.

However these kinds of dependencies are very

generic and are proposed as a basis to track input ar-

tifacts and output artifacts produced through process

invocations. To distinguish several causal dependen-

cies of the same kind, OPM allows to annotate used or

wasGeneratedBy dependencies with syntactic roles.

A Role is deﬁned in OPM as a particular function of

an artifact (or an agent) in a process. The OPM model

does not formally deﬁne roles but allows to “tag”

dependencies between artifacts (or agents) and pro-

cesses with meaningful labels. In OPM the execution

of the sample registration process illustrated in Fig-

ure 1 could be translated with these two statements

“Registration

Process

: used( f loating) : Image

Arti f act

”

and “Registration

Process

: used(re f erence) :

Atlas

Arti f act

”. The syntactic roles “ f loating”

and “re f erence” aim at distinguishing how artifacts

are related to processes, but their meaning remains

highly dependent on their usage within a given

process, and thus, remain highly domain-speciﬁc.

In the following section we propose to go deeper

with the notion of roles, through the proposition of

a taxonomy of Roles in neuroimaging that aims at

(i) enhancing the semantic annotation of services,

and (ii) exploiting OPM provenance information

to deduce meaningful statements in the context of

neuroimaging workﬂows.

5 ROLE CONCEPTS

To beneﬁt from expert knowledge conceptualized

through a domain ontology (such as the OntoNeu-

roLOG ontology), services involved in e-Science

workﬂows are manually associated to concepts of

the ontology. Semantically annotating a service con-

sists in using an ontology to bind technical concepts,

i.e. elements syntactically describing services, to

domain-speciﬁc concepts. Most of semantic web-

services initiatives, namely OWL-S, WSMO, SWSO,

or SAWSDL, distinguish the annotation of the func-

tionality of the service from the annotation of the ser-

vice parameters which consume or produce data. For

instance, let us consider a medical image processing

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

Figure 3: A Role taxonomy characterizing how neuroimaging data can be related to neuroimaging processing tools.

tool performing a de-noising operation. From a tech-

nical or syntactical point of view, the service might

be implemented by an executable binary taking as in-

put a raw ﬁle materializing a noised medical image

and producing as output another raw ﬁle materializ-

ing the resulting de-noised image. From a seman-

tic point of view, this de-noising service might im-

plement a particular kind of algorithm characterizing

how the image is processed. This “how” should be

described through the annotation of the functional-

ity of the service, i.e. a particular class of restoration

processing. The service might additionally require a

speciﬁc medical image format, and a speciﬁc modal-

ity of acquisition, for instance ultrasound. Moreover,

the resulting de-noised image should preserve the in-

put modality; in other words, even de-noised, the im-

age still remains an ultrasound image. The service in-

put/output parameters are usually annotated with con-

cepts describing the nature of consumed or produced

data. We will see in the following section that such se-

mantic annotation of the nature of consumed or pro-

duced data is often not sufﬁcient to be precisely ex-

ploited to produce new domain-speciﬁc annotations.

5.1 Differentiating Natural and Role

Concepts

Service annotation should also make explicit how

consumed or produced data items are related to the

processes. For instance, if we consider the registra-

tion service involved in the workﬂow shown in Fig-

ure 1, both input parameters should share the same

intrinsic nature. Indeed, in this example, the refer-

ence image parameter and the ﬂoating image param-

eter have been acquired both through the same Mag-

netic Resonance modality (MR) and should be mate-

rialized with the same ﬁle format. In this geometrical

realignment procedure, the two input parameters are

not distinguished by their intrinsic nature but rather by

their relationship to the registration process, namely

ﬂoating and reference images. It is important to note

that these two concepts only make sense in the con-

text of a particular kind of image processing, registra-

tion. Without the knowledge of “which data is acting

as the reference image” or “which data is acting as the

ﬂoating image”, it is difﬁcult to deduce any meaning-

ful information from the execution of the registration

workﬂow, such as “this resulting image can be super-

imposed with this reference image”, or more gener-

ally to retrieve images that have been registered with

the same reference and thus, that can be superimposed

together.

To tackle this issue we propose to distinguish Nat-

ural concepts and Role concepts when annotating se-

mantic service parameters by relying on a domain-

speciﬁc role taxonomy.

Figure 3 illustrates the taxonomy of roles dedi-

cated to the characterization of the relationships be-

tween neuroimaging data and their dedicated process-

ing. Role concepts are organized following the main

classes of neuroimaging processing like in the On-

toNeuroLOG dataset processing ontology.

Relying on this taxonomy of roles, we are now

able to precisely annotate the input and output pa-

rameters of our image registration service with both

Natural concepts and Role concepts. Both input im-

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER

DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME

ages are characterized by a same Natural concept, T1

weighted magnetic resonance image (T1-MR). T1-

MR can be considered as a Natural concept because it

stands on its own and does not characterize how input

data are related with any other entities. On the other

hand, service input parameters can be annotated with

two distinct Role concepts to characterize how input

data are related to the registration process. The ser-

vice input parameter interpreting data as ﬂoating (the

moving data, that will ﬁnally be realigned) is anno-

tated with role As-ﬂoating-image, and the second ser-

vice input parameter interpreting data as the geomet-

rical reference is annotated with role As-reference-

image. Figure 4 illustrates the annotation with Role

concepts for the two services involved in the full reg-

istration use-case workﬂow.

Registration Re-sampling

100

As-resampled

As-unprocessed

As-ﬂoating

As-transformation

As-reference

As-transformation

Figure 4: Roles involved in the registration workﬂow.

Disambiguating the semantic annotation of ser-

vices, we present in the following section how Role

concepts are the basis to instrument domain ontolo-

gies with reusable inference rules, producing new

meaningful statements.

5.2 Integration of OPM and

OntoNeuroLOG

The Roles taxonomy also acts as a bridge ontology,

articulating the two technical ontologies dedicated to

the description of services (Web Services) and to the

provenance information associated to their invocation

(OPM). Indeed, Role concepts are associated to the

service I/Os (input/output-variable) through the same

property (refers-to) as used to describe the nature

of consumed/produced data (OntoNeuroLOG Dataset

ontology). Moreover, Role concepts are directly ex-

tending the OPM Role class, so that when recording

provenance at workﬂow runtime, the workﬂow enac-

tor is able to link Artifacts to Processes through the

newly reﬁned Roles.

5.3 Reusable and Service Independent

Inference Rules

The use of rule engines (inference engines) is a well

adopted data-driven and declarative approach to de-

duce new conclusions and thus produce new facts

from a set of statements. In an OPM-instrumented

execution engine, the invocation of services generates

provenance statements such as the ones illustrated in

Figure 5 for the registration workﬂow. The graphical

syntax introduced by (Moreau et al., 2011) is reused:

Artifacts are represented by ellipses and Processes are

represented by rectangles, used and wasGeneratedBy

causal dependencies, parametrized with roles, are rep-

resented by plain arrows.

Registration

Image

Atlas

used

(As_ﬂoating_image)

used

(As_reference_image)

Matrix

wasGeneratedBy

(As_afﬁne_transformation)

Resampling

Image

Matrix

used

(As_unprocessed)

used

(As_afﬁne_transformation)

Result

wasGeneratedBy

(As_resampled_image)

Figure 5: OPM statements recorded through the invocation

of the registration workﬂow.

To automate the production of a statement linking

the resulting data to the source data through a domain-

speciﬁc property, an inference rule is written using the

role-parameterized provenance causal dependencies.

For instance, Figure 6 illustrates the inference rule

deducing the can be superimposed with property in

the case of the registration workﬂow. The left part of

the implication, the antecedent corresponds to the If

clause of the inference rule and consists in identify-

ing a conjunction of statements necessary to produce

the statements expressed in the consequent, the right

part of the implication (the Then clause of the rule).

The ﬁrst two lines assert that processes must refer,

for the ﬁrst one, to a Registration treatment, and for

the second one, to a Resampling treatment. In other

words, the services invoked by the processes should

have been annotated with the corresponding Natu-

ral concepts of the OntoNeuroLOG domain ontology.

The two following lines of the If clause identify arti-

facts and processes through their Role concepts: the

resulting image is identiﬁed through As-resampled-

image, the registration matrix is identiﬁed through

As-afﬁne-transformation, and the reference image is

identiﬁed through As-reference-image. Finally, when

the reference image and the resulting resampled im-

age are identiﬁed, the rule engine is able to produce a

new statement saying that both images can be super-

imposed (can

be superimposed with property of the

OntoNeuroLOG ontology).

Using Role concepts, domain ontologies can be

instrumented with inference rules which remain ser-

vice independent. Such inference rules can be reused

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

RegistrationAtlas

used

(As_reference_image)

Matrix

wasGeneratedBy

(As_afﬁne_transformation)

ResamplingMatrix

used

(As_afﬁne_transformation)

Result

wasGeneratedBy

(As_resampled_image)

∧

Resampling Resampling-Class

Registration Registration-Class

∧

⇒

‣

Result

can_be_superimposed_with

Atlas

Figure 6: Reusable inference rule automating the annotation

of superimposable images.

in the context of several service implementations real-

izing a same kind of treatment. Let us consider the de-

ployment of a new registration service, implemented

with a new algorithm. As soon as this new service

is annotated with Role and Natural concepts of the

same class as (or subsumed by) the concepts appear-

ing in the registration inference rule, there is no need

to rewrite an inference rule speciﬁc to this particu-

lar service. As a consequence, workﬂows involving

this new service will also beneﬁt from the generation

of annotations stating the “superimposability” of data.

With this approach, domain expert can equip their on-

tologies with inference rules that provide meaningful

information to end-users independently from the ser-

vices deployed. Service providers can focus on their

services, transparently reusing such high-level infer-

ence rules.

6 IMPLEMENTATION

6.1 System Architecture

Figure 7 schemes the NeuroLOG platform, with a par-

ticular focus on its semantic components aiming at

enhancing the sharing and enactment of neuroimag-

ing workﬂows. This deployment shows three col-

laborating sites A, B, C and end-users interacting

with their proper site gateway (Site A) through the

client application. Processing tools are syntactically

described and instrumented as relocatable bundles

through jGASW (Rojas Balderrama et al., 2010) to

enable their deployment and invocation on various

computing infrastructure. The MOTEUR (Glatard

et al., 2008) component enables the design of new

experiments as scientiﬁc workﬂows and is respon-

sible for their enactment. The semantic annotation

of jGASW services is realized through a dedicated

user interface of the client application (Service an-

notator) while the workﬂow enactor is responsible

for recording provenance information at runtime and

populating the semantic store with OPM RDF state-

ments. Semantic annotations are managed through lo-

cal RDF triple stores implemented with the Jena API.

The CORESE semantic engine (Corby et al., 2004)

is used to perform semantic querying and reasoning

over the knowledge base. CORESE is a semantic

query/rules engine based on conceptual graphs, sup-

porting RDF(S) entailments and a subset of OWL-

Lite entailments: datatypes, transitivity, symmetry

and inverse properties.

Collaborating Site AClient application

Service wrapping

(jGASW)

Workﬂow designer

and enactor

(MOTEUR)

Semantic query client

and service annotator

Computing and storage infrastructure (EGI grid)

Middleware

(NeuroLOG)

Semantic store

Semantic engine

(CORESE)

Collaborating

Site C

Collaborating

Site B

Middleware

stack

Middleware

stack

Ontology

(OntoNeuroLOG)

Figure 7: NeuroLOG platform: semantic enhancements to

support the sharing and enactment of neuroimaging work-

ﬂows.

6.2 MOTEUR-S

This section presents the extension of the MOTEUR

workﬂow environment to (i) annotate and catalog

jGASW services and (ii) track and query provenance

information through the OPM standard. This work

is based on the integration of the JSPF plugin frame-

work within MOTEUR, allowing third-party develop-

ers to integrate repository or listener plugins. Reposi-

tory plugins are dedicated to extending the sources of

composable services, and listener plugins (based on

the observer design pattern) are dedicated to monitor

workﬂow states at runtime, and trigger speciﬁc pre-

or post-processing.

6.2.1 Semantically Annotating and Cataloging

Processing Tools

The annotation task, generally performed by the pro-

cessing tool provider, consists in associating to each

service port through a dedicated GUI, at maximum

one Natural concept specifying the semantic nature

of the consumed/produced data, and at maximum one

Role concept characterizing how data is related to the

service through this particular port. It is not desir-

able to associate more than one Natural concept or

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER

DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME

one Role concept to a given service port since it will

conduct to an ambiguous semantic description of the

service. From the service point of view, it would

not be possible to determine which Natural or Role

concept characterizes the consumed or produced data.

Semantic descriptions can then be saved as a collec-

tion of RDF statements, or directly published into the

semantic store.

To enhance the overall coherency of workﬂows

at design time, the semantic service catalog can be

queried to retrieve services realizing a particular kind

of treatment (through an associated Natural concept

of the domain ontology), or to retrieve services able

to consume a particular kind of data at a given step of

the workﬂow construction.

6.2.2 Recording and Querying Provenance

Information

When a workﬂow is started, a new OPM account is

registered and timestamped. One OPM account is cre-

ated per workﬂow invocation, thus easing the retrieval

of all provenance annotations generated in the context

of a single workﬂow invocation. For each process

invocation, we register an OPM process entity, also

timestamped, and its consumed and produced data

as OPM artifacts linked to the OPM process through

their corresponding causal dependencies used or was-

GeneratedBy. For each causal dependency, we as-

sociate an OPM role corresponding to the Role con-

cept used to annotate the service description. Finally,

OPM processes are linked through an OPM wasCon-

trolledBy entity to an OPM agent. This agent cor-

responds to the service description identiﬁed by the

WSDL URL of the jGASW service deployed. Se-

mantic service description being also identiﬁed by the

WSDL URL, the system is thus able to retrieve, from

an OPM process and the semantic catalog of anno-

tated services, all available domain-speciﬁc annota-

tions (Natural and Role concepts) describing the in-

voked service.

The CORESE semantic engine is used to per-

form generic queries to retrieve, for instance, from

the workﬂow results, the source data that has been

derived through the data analysis workﬂow. In prac-

tice we rely on SPARQL 1.1 property path expres-

sions which provide a compact and powerful language

to handle complex graph matching, such as alterna-

tive/optional paths, or path length constraints.

6.3 New Knowledge Generation

CORESE provides a forward chaining engine that,

from a set of inference rules, saturates its conceptual

graph until no new statements can be inferred. Infer-

ence rules are expressed through the CORESE rule

syntax (non-XML but SPARQL-like), which is very

similar to the SWRL proposal from the W3C, also

describing an implication between an antecedent and

a consequent. However, CORESE rules differ since

they beneﬁt from a limited support of CORESE for

OWL-Lite entailments.

In this ﬁrst implementation, we assume that infer-

ence rules instrumenting the domain ontology are pro-

vided by the ontology developers and are thus pack-

aged within the domain ontology as complementing

ﬁles. Inference rules could be applied to extend the

knowledge base at any time. Rather than letting the

end-user select the suitable inference rule, and trigger

the application of the rule, all available rules are sys-

tematically applied, triggered by the end of a work-

ﬂow invocation through a speciﬁc MOTEUR event.

We consider this automatic application of rules be-

cause if the antecedent of the rule is not matched, then

the rule is not applicable. On the other hand, when an

antecedent is matched, it makes sense to apply the

rule since it has been provided by the ontology devel-

opers and has been designed to serve the concerns of

the whole user community.

When the MOTEUR listener plugin dedicated to

provenance annotations is notiﬁed with the end of a

workﬂow invocation, the CORESE semantic engine

is populated with (i) the domain ontology (covering

both Nature and Role concepts) and OPM provenance

ontology, with (ii) all available inference rules pro-

vided with the domain ontology, and (iii) with state-

ments describing the annotated services and OPM

statements describing the workﬂow invocation. Then

the forward chaining engine of CORESE is started to

produce new inferred statements.

7 CONCLUSIONS AND

PERSPECTIVES

E-Science experimental platforms strongly rely on

Service Oriented Architectures to assemble ﬂows of

data analysis services. However, their usability is

hampered by the level of expertise of experiment de-

signers, as they are expected to have a clear under-

standing of the semantics of the data processing, i.e.

what kind of data is processed and how they are effec-

tively processed. To improve their usability and as-

sist end-users, ontologies, semantic annotations and

reasoning engines are integrated. In this paper, we

proposed a clear delineation between Role and Natu-

ral concepts in the domain ontology to disambiguate

semantic annotation of service parameters. In addi-

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

tion, the domain ontology can be instrumented with

inference rules that leverage the description of Roles

combined with generic provenance information to en-

rich our semantic repository with meaningful domain-

speciﬁc annotations at runtime.

Since this work was implemented in the context

of the NeuroLOG experimental platform, the service

considered for annotations are web services wrapped

with jGASW, a legacy processing tools wrapper tar-

geting large scale distributed infrastructures. How-

ever our approach is more widely applicable, and

could be implemented using standard web services

described through standard service ontologies.

Regarding the sharing of the Role taxonomy of

neuroimaging data with other user communities, two

approaches could be considered as a continuation of

this work: (i) the creation of an OPM proﬁle dedi-

cated to the neuroimaging domain, and (ii) the articu-

lation of the OPM ontology with the DOLCE founda-

tional ontology.

OPM proﬁles constitute a good opportunity to

share knowledge associated to the role of neuroimag-

ing data. Indeed, an OPM neuroimaging proﬁle could

be constituted with the two subsets of the OntoNeu-

roLOG ontology supporting this work, the Dataset

ontology to extend OPM Artifacts, and the Dataset-

processing ontology to extend OPM Processes. The

Role taxonomy proposed in this paper could be inte-

grated almost directly.

The second approach, more conceptual, would

consist in proposing an OPM ontology whose main

classes are grounded to foundational ontologies such

as DOLCE or BFO (Basic Formal Ontology). It

would allow to smartly articulate OPM and domain

ontologies based on foundational ontologies such as

BIOTOP (Top-Domain ontology for the life sciences)

or OBI (Ontology of Biomedical Investigation) life

science ontologies, and thus exploit these ontologies

at workﬂow runtime. Indeed considering our ap-

proach from an ontology design perspective, a signiﬁ-

cant effort is still needed for a complete integration in

the OntoNeuroLOG framework, since Role concepts,

designated through the refers-to property, should con-

form to the DOLCE foundational ontology and its re-

lated core ontologies. This ontology integration task

could also cover the semantic overlap between OPM

Artifacts and OntoNeuroLOG Datasets. However, in

the context of this work, the CORESE semantic en-

gine can still (i) retrieve service description, or prove-

nance statements through SPARQL queries and (ii)

produce new meaningful statements through its infer-

ence engine.

The concepts developed in this paper are currently

being integrated in a prototype platform. In the fu-

ture, its use in production in the context of the Virtual

Imaging Platform (VIP project) will enable the eval-

uation of our approach. We plan to study the impact

of Role concepts on four actors in the system: the ser-

vice providers, the workﬂow designers, the ontology

and inference rule designers, and the ﬁnal end-users

realizing e-Science workﬂows. Indeed, we plan to

measure if Role concepts are actually used by service

providers to annotate their processing tools, and if

they enable to disambiguate service parameter anno-

tations, to ﬁnally enable more accurate results when

workﬂow designers query the semantic catalog of ser-

vices. Moreover, we plan to evaluate if Role concepts

are actually involved by ontology designers into in-

ference rules to produce new domain speciﬁc state-

ments. Finally, we plan to evaluate the production of

new annotations at workﬂow runtime, and its useful-

ness from the end-user perspective through the analy-

sis of the semantic queries. More precisely, we want

to determine if the targets of the semantic queries are

annotations inferred from rules involving roles, or if

the targets are annotations produced by other means.

Initially applied to computational neurosciences,

this work goes beyond this scope, as same principles

are planned to be applied in the context of the VIP

project, which targets medical image acquisition sim-

ulation. It is envisaged to validate the applicability

and usability of the delineation of Role and Natural

concepts in domain ontologies to (i) ease the design of

simulation workﬂows (e.g. simulated cardiac images

through ultrasound modality) and (ii) extend semantic

repositories with new meaningful statements describ-

ing either simulated data or the simulated organs and

their constituting anatomical entities.

ACKNOWLEDGEMENTS

This work was funded by the French National Agency

for Research under grants ANR-06-TLOG-024 (Neu-

roLOG project) and ANR-09-COSI-03 (VIP project).

REFERENCES

Battle, S., Bernstein, A., Boley, H., Grosof, B.,

Gruninger, M., Hull, R., Kifer, M., Martin, D.,

McIlraith, S., McGuinness, D., Su, J., and Tabet,

S. (2005). Semantic Web Services Ontology

(SWSO) [http://www.w3.org/submission/swsf-swso].

http://www.w3.org/Submission/SWSF-SWSO.

Bhagat, J., Tanoh, F., Nzuobontane, E., Laurent, T., Or-

lowski, J., Roos, M., Wolstencroft, K., Aleksejevs, S.,

Stevens, R., Pettifer, S., Lopez, R., and Goble, C. A.

(2010). BioCatalogue: a universal catalogue of web

CHARACTERIZING SEMANTIC SERVICE PARAMETERS WITH ROLE CONCEPTS TO INFER

DOMAIN-SPECIFIC KNOWLEDGE AT RUNTIME

services for the life sciences. Nucleic Acid Research,

Article in press.

Corby, O., Dieng-Kuntz, R., and Faron-Zucker, C. (2004).

Querying the Semantic Web with Corese Search En-

gine. In ECAI, pages 705–709.

Glatard, T., Montagnat, J., Lingrand, D., and Pennec, X.

(2008). Flexible and efﬁcient workﬂow deploye-

ment of data-intensive applications on grids with MO-

TEUR. International Journal of High Performance

Computing Applications (IJHPCA) IF=1.109 Special

issue on Special Issue on Workﬂows Systems in Grid

Environments, 22(3):347–360.

Gordon, P. M. K. and Sensen, C. W. (2008). Creating Bioin-

formatics Semantic Web Services from Existing Web

Services: A Real-World Application of SAWSDL. In

ICWS, pages 608–614.

Gruninger, M., Hull, R., and McIlraith, S. (2008). A Short

Overview of FLOWS: A First-Order Logic Ontology

of Web Services. IEEE Data Engineering Bulletin.,

31(3):3–7.

Henriksson, J., Pradel, M., Zschaler, S., and Pan, J. Z.

(2008). Ontology Design and Reuse with Conceptual

Roles. In Proceedings of the 2nd International Con-

ference on Web Reasoning and Rule Systems, RR ’08,

pages 104–118, Berlin, Heidelberg. Springer-Verlag.

Kassel, G. (2005). Integration of the DOLCE top-level

ontology into the OntoSpec methodology. Technical

Report 2005-10-18, University of Picardie, Amiens,

France.

Kopeck

y, J., Vitvar, T., Bournez, C., and Farrell, J. (2007).

SAWSDL: Semantic Annotations for WSDL and

XML Schema. IEEE Internet Computing, 11(6):60–

67.

Lord, P., Alper, P., Wroe, C., and Goble, C. (2005). Feta:

A Light-Weight Architecture for User Oriented Se-

mantic Service Discovery. In European Semantic Web

Conference, pages 17–31. Springer Berlin / Heidel-

berg.

Martin, D., Burstein, M., McDermott, D., McIlraith, S.,

Paolucci, M., Sycara, K., McGuinness, D. L., Sirin,

E., and Srinivasan, N. (2007). Bringing Semantics

to Web Services with OWL-S. World Wide Web,

10(3):243–277.

Montagnat, J., Gaignard, A., Lingrand, D., Rojas Balder-

rama, J., Collet, P., and Lahire, P. (2008). NeuroLOG:

a community-driven middleware design. In Health-

Grid, pages 49–58, Chicago. IOS Press.

Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y.,

Groth, P., Kwasnikowska, N., Miles, S., Missier, P.,

Myers, J., Plale, B., Simmhan, Y., Stephan, E., and

den Bussche, J. V. (2011). The Open Provenance

Model core speciﬁcation (v1.1). Future Generation

Computer Systems, 27(6):743 – 756.

Rojas Balderrama, J., Montagnat, J., and Lingrand, D.

(2010). jGASW: A Service-Oriented Framework

Supporting High Throughput Computing and Non-

functional Concerns. In IEEE International Confer-

ence on Web Services(ICWS 2010), Miami, FL, USA.

IEEE Computer Society.

Roman, D., de Bruijn, J., Mocan, A., Lausen, H.,

Domingue, J., Bussler, C., and Fensel, D. (2006).

WWW: WSMO, WSML, and WSMX in a Nutshell.

The Semantic Web – ASWC 2006, pages 516–522.

Sheth, A. P., Gomadam, K., and Ranabahu, A. (2008). Se-

mantics enhanced Services: METEOR-S, SAWSDL

and SA-REST. IEEE Data Eng. Bull., 31(3):8–12.

Sowa, J. F. (1984). Conceptual structures: information pro-

cessing in mind and machine. Addison-Wesley Long-

man Publishing Co., Inc., Boston, MA, USA.

Temal, L., Dojat, M., Kassel, G., and Gibaud, B. (2008). To-

wards an ontology for sharing medical images and re-

gions of interest in neuroimaging. Journal of Biomed-

ical Informatics, 41(5):766–778.

Vitvar, T., Kopecky, J., Viskova, J., and Fensel, D. (2008).

WSMO-Lite Annotations for Web Services. In 5th Eu-

ropean Semantic Web Conference (ESWC2008), pages

674–689.

Withers, D., Kawas, E., McCarthy, L., Vandervalk, B.,

and Wilkinson, M. (2010). Semantically-guided

workﬂow construction in Taverna: the SADI and

BioMoby plug-ins. In Proceedings of the 4th interna-

tional conference on Leveraging applications of for-

mal methods, veriﬁcation, and validation - Volume

Part I, ISoLA’10, pages 301–312, Berlin, Heidelberg.

Springer-Verlag.

Wolstencroft, K., Alper, P., Hull, D., Wroe, C., Lord, P. W.,

Stevens, R. D., and Goble, C. A. (2007). The

Grid

ontology: bioinformatics service discovery. IJBRA,

3(3):303–325.

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development