A PRACTICAL ONTOLOGY-DRIVEN WORKFLOW

COMPOSITION FRAMEWORK

Huy Pham, Deborah Stacey and Rozita Dara

School of Computer Science, University of Guelph, Guelph, Canada

Keywords:

Workﬂow composition, Ontology-driven planning framework, Planning ontology, Ontology-driven workﬂow

composition, Workﬂow composition.

Abstract:

Existing planning-based approaches to ontology-driven workﬂow composition (ODWC) integrate planning

into their frameworks and ontologies in ways that are either less reusable or ineffective. A more modular and

reusable design is possible, but a successfull application of this design requires addressing some important

practicality issues. In this paper, we provide a comprehensive survey of existing approaches to intelligent

ontology-driven workﬂow composition, discuss the practicality challenges associated with building ontology-

driven and planning-based workﬂow composition systems, and present a reusable and practical ontology-

driven planning framework that could be used for such purposes.

1 INTRODUCTION

There are many application domains where a major

activity is the creation or arranging of sequences of

actions or events. This activity of temporal planning

is often referred to as workﬂow composition. Busi-

ness processes are often conceptualized as a workﬂow

and thus tools for helping with workﬂow composition

will ﬁnd a wide and appreciative audience.

From a computational perspective, workﬂow com-

position is the task of coming up with an appropriate

sequence of (computational) tasks that accomplish a

given set of objectives. A simple example is the pro-

cess that a data analyst may go through when faced

with a data mining task. The analyst may employ

several data cleaning programs to make sure that the

data is consistent and in the proper format. They will

then use these data sets as input to a series of different

analysis programs and then send the output of these

programs to a visualization application to display the

results in a format accessible for business analysts.

Recent work in this area has taken advantage

of ontologies to drive this composition process.

But many existing approaches to planning-based

Ontology-Driven Workﬂow Composition (ODWC)

are not totally adequate for building effective real-

world workﬂow composition systems. Our analysis

leads us to believe that there are two major gaps that

need to be addressed in going from a domain expert

with a problem to solve and a system that allows that

expert to translate their domain problem into a com-

putational/computing problem that can generate a de-

sign for an appropriate workﬂow composition.

In this paper, we will provide a comprehensive

survey of existing approaches to intelligent ontology-

driven workﬂow composition and classify them into

four major approaches. We will then discuss the

challenges associated with building ontology-driven

and planning-based workﬂow composition systems

and identify features that are desirable in an effec-

tive ODWC framework. We then present our reusable

and practical ontology-driven planning framework

that incorporates all these features to provide the

ODWC system designer with a practical, effective

and friendly means of building ODWC systems. This

framework also addresses the important theoretical

challenges that are inherent to the task of doing goal-

based reasoning in ontology-driven applications. We

hope that our framework functions as a practical way

to address this challenge using existing and mature

technologies.

2 ONTOLOGY-DRIVEN

WORKFLOW COMPOSITION

In this section, we start out by reviewing some of the

most notable works in ODWC and make some obser-

vations about their advantages and limitations. We

242

Pham H., Stacey D. and Dara R..

A PRACTICAL ONTOLOGY-DRIVEN WORKFLOW COMPOSITION FRAMEWORK.

DOI: 10.5220/0003659102420248

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 242-248

ISBN: 978-989-8425-80-5

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

then place ourselves in the shoes of an ODWC sys-

tem designer and explore the list of features that are

desirable and important.

2.1 Existing Works

From an AI perspective, workﬂow composition is

a form of sequential decision making (Russell and

Norvig, 2002), and as such can be approached us-

ing various existing knowledge-based planning tech-

niques (Brachman and Levesque, 2004). The main

drawback of such an approach however, is that it re-

quires the knowledge bases – the collection of domain

knowledge needed to support the workﬂow composi-

tion process – to be implicitly encoded in a language

that is speciﬁc to the planning framework employed,

and therefore less reusable to other applications (Gib-

son and Stevens, 2009).

In light of this reusability concern, the Semantic

Web community has been promoting the use of on-

tologies (Horrocks, 2008) as its key knowledge rep-

resentation mechanism. This mechanism provides

a standardized and well-supported facility that al-

lows domain knowledge to be described in a generic,

application-independent way that it can be easily un-

derstood, shared and reused by many different appli-

cations, including future applications for which the

knowledge was not originally intended.

Because of this important advantage that ontolo-

gies offer, there has been a lot of interest in repre-

senting the knowledge bases of workﬂow composi-

tion systems using ontologies instead of framework-

speciﬁc and application-speciﬁc languages, and con-

sequently, a large number of works have been re-

ported on ontology-driven workﬂow and system com-

position. The remaining part of this section provides

a review of some of the most notable works, as well

as some general observations about the common lim-

itations found in these works.

Most existing works in ODWC use the same over-

all approach: they wrap the various workﬂow ac-

tivities into logical units, called compositional units

(CU), adorn each CU with an appropriate set of at-

tributes (e.g. name, function, input data format, etc.),

organize them into some hierarchical structure and

serve them up – via an ontology – as a set of build-

ing blocks from which a workﬂow can be constructed.

Where they differ is the mechanism through which

each of these frameworks produce their workﬂows. In

this respect, existing works on ODWC can be roughly

divided into four different approaches.

2.1.1 Interactive Composition

In this approach, workﬂows are composed in an inter-

active fashion using manual inputs from the user. The

software composition framework described in (Hlo-

mani and Stacey, 2009) is one such example. This

framework aims to provide the foundations needed to

enable non-technical domain experts to rapidly com-

pose and modify their own software systems from

individual building blocks that represent the various

computing algorithms. More speciﬁcally, the frame-

work uses an ontology to formally describe the vari-

ous computing algorithms, and offers them as a set of

building blocks from which the user can visually com-

pose her computational system by selecting and con-

necting individual algorithmic components together.

One of the main limitations of this approach is that

while intelligent assistance is provided, the user is still

required to come up with the design.

2.1.2 Template-based Composition

In the second approach, workﬂows are selected from

a library of pre-built workﬂow templates and sug-

gested to the user as a guideline for composing her

system. This approach is represented by the data

mining workﬂow composition framework described

in (Morik and Scholz, 2003). The primary rationale

behind this approach is that successful and effective

workﬂows, once built, tested and tuned by experts,

could, and should, be shared and reused as guiding

templates for non-expert users in similar applications.

The main advantage of this approach is that such a

framework can act as a facility for easily capturing,

sharing and reusing effective data mining workﬂows.

The main limitation is that user assistance can only be

provided for situations in which a similar and appro-

priate template can be found in the library.

2.1.3 Planning-based Composition via Direct

Translation

In this approach, workﬂow composition is treated as

a planning problem and a planning mechanism is em-

ployed to generate workﬂows. Some of the most no-

table approaches are described in (Bernstein et al.,

2005), (

akov

a et al., 2008) and (Diamantini et al.,

2009). The main draw back of this approach is that

typically all planning-related knowledge is implic-

itly encoded into the primary knowledge base (i.e.

the ontologies used to describe the compositional

units). Because planning-related knowledge is appli-

cation and problem speciﬁc, embedding them into the

main ontologies makes these ontologies application-

A PRACTICAL ONTOLOGY-DRIVEN WORKFLOW COMPOSITION FRAMEWORK

243

and purpose-dependent and, as a consequence, less

reusable for future applications.

2.1.4 Planning-based Composition via a

Planning Ontology

This approach is represented by the works described

in (Gil et al., 2000) and (Rajpathak and Motta,

2004). These papers promote the use of a dedicated

and generic Planning Ontology to describe planning-

related knowledge.

In (Gil et al., 2000), the authors describe an ontol-

ogy that contains language constructs for describing,

among other things:

• Objectives, subobjectives, and objectives decom-

position

• Tasks (actions), task constraints, and tasks-

accomplish-objectives types of relationships

• Decision points, ordering of tasks, temporal con-

straints

The strong point about this ontology is that the list

of language constructs it provides is comprehensive

and powerful enough to describes complex plans. The

main limitation of this work is that because its main

intent was to describe plans as opposed to planning

problems (the ﬁrst is a solution of the second), the on-

tology lacks the necessary constructs (pre-conditions,

effects, world’s dynamics, cost/reward, etc.) needed

to drive a planner, and therefore is inadequate for the

purpose of building a planning-based ODWC system.

2.2 Desirable Features of an ODWC

Framework

In this section, we place ourselves in the shoes of

the ODWC system designer and explore the list of

features that are important or desirable for ODWC

systems. We will do this by trying to understand

the types of support the system designer would need

in order to build an effective and practical planning-

based ODWC system.

On the architectural side, the system designer

needs a framework that allows him to create simple

and reusable knowledge bases, as well as a framework

that can be employed to build WC systems in differ-

ent domains without extensive modiﬁcations or cus-

tom coding. On the representation side, he will need a

comprehensive set of language constructs for describ-

ing real-world workﬂow composition problems. On

the reasoning side, he will need a practical mecha-

nism to do goal-based reasoning in his system.

2.2.1 Clear Separation between Planning

Knowledge and other Domain Knowledge

This approach offers at least 3 important advantages

over previous approaches to planning-based ODWC.

1. Increased reusability of domain knowledge: Be-

cause the domain and objectives ontologies are

independent from the application that uses them,

they are much more reusable for other applica-

tions.

2. Easier to build descriptive domain ontologies:

Because the ontology designers can focus on de-

scribing the domains without having to worry

about how those descriptions will be used later,

high quality and descriptive domain ontologies

become easier to build.

3. Increased reusability and sharability of planning

knowledge and strategies: Because the syntax and

language constructs would allow planning prob-

lems to be declaratively described in a manner

that is completely independent from the imple-

mentation of the workﬂow composition engine,

the Planning Ontology has the potential to act as

a common abstraction language in which plan-

ning problems and strategies can be encoded in

a sharable and reusable way.

2.2.2 Planning Knowledge should be Captured

using an Ontology instead of Directly in

the Underlying Planning Formalism

The use of a planning ontology would allow the work-

ﬂow composition problem, and the logic for how to

solve it, to be modelled independently from the un-

derlying planning paradigm. This separation is a very

important feature because it would allow the under-

lying translation framework to be reused for building

other ODWC systems without requiring a major mod-

iﬁcation to its logic. All that needs to be modiﬁed

instead is the ontological problem description.

2.2.3 Should provide a Comprehensive Set of

Language Constructs to Effectively

describe Real-world Workﬂow

Composition Problems

As discussed earlier, automated workﬂow composi-

tion, from a computational point of view, can be

considered a planning problem (Russell and Norvig,

2002) and, as such, can be modelled or described in

pretty much the same way as a planning problem.

A typical workﬂow composition problem can be ad-

equately modelled using the following types of de-

scriptions:

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

244

• Workﬂow Actions

• Workﬂow Actions Pre-Conditions

• Workﬂow Actions Effects (aka, Post-Conditions)

• Initial State

• Goal State

Once the above model has been built, it can be fed

into the workﬂow composition engine (WCE), which

searches through the many possible combinations of

action sequences and comes up with an appropriate

workﬂow that solves the problem.

In order to be able to build effective ODWC sys-

tems that can handle complex, real-world problems,

the system designer will need a comprehensive set of

language constructs that can effectively describe, at a

minimum, all the knowledge types above. While ex-

isting ontology-driven planning frameworks do pro-

vide some of these constructs, they still lack some

very important ones, including those needed to de-

scribe the world’s states (e.g. initial state, goal state),

state transitions, as well as those needed for advanced

planning techniques such as heuristics.

2.2.4 Should provide a Practical Mechanism for

doing Goal-based Reasoning in ODWC

Applications

From a knowledge representation and reasoning per-

spective, workﬂow composition boils down to the task

of proving the following entailment:

KB  ∃w Valid(w) ∧ AchieveGoal(w)

where w = [a1, a2, . . . , aN] is the workﬂow the user

is interested in, and KB is the knowledge base repre-

senting the workﬂow composition problem.

While several other knowledge-based formalisms

provide native support for this kind of reasoning, it

is not easily attainable in an ontology-driven appli-

cation. Based closely on Description Logics (DL),

ontologies were intended as a formalism for describ-

ing (object-oriented) worlds and is not well-suited for

goal-based reasoning tasks such as this.

First Order Logic (FOL) is the most expressive

language compared to DL and Horn Logic (HL) but

is undecidable. DL and HL are less expressive yet de-

cidable subsets of FOL. These two languages can be

thought of as two different ways of attaining decid-

ability via limiting the scope of the language. Due

to the difference in the ways their scopes are lim-

ited, each of these two languages are well-suited for

different purposes – DL is well-suited for describing

(object-oriented) worlds, while HL is well-suited for

goal-based reasoning and hence problem solving.

While several approaches have been proposed to

integrate goal-based reasoning (e.g. Rules) into De-

scription Logics (c.f. the Semantic Web Rule Lan-

guage (SWRL) (Horrocks et al., 2004), and Descrip-

tion Logic Programs (DLP) (Grosof et al., 2003)),

their success to date is still somewhat limited. As

discussed in (Hitzler and Parsia, 2009), this is a non-

trivial task. The major challenge is that modifying or

extending the underlying language (DL in this case)

could lead to undecidability. SWRL, for example, is

undecidable and, as a consequence, has no native rea-

soner. Even more importantly, introducing a new ex-

tension to a language often entails some difﬁcult and

time-consuming tasks. First, all the theoretical results

would need to be carefully mapped out and secondly,

the toolset will be need to be updated/augmented.

Thirdly, getting users to accept the new language is

also a highly non-trivial task. It usually takes a long

time for a language or formalism to attain the needed

critical mass for widespread adoption.

Due to these inherent difﬁculties, hybrid ap-

proaches are often considered when goal-based rea-

soning is needed within an ontology-driven applica-

tion. In section 3 below, we propose a hybrid (i.e.

translational) solution for integrating goal-based rea-

soning into ODWC systems.

3 A PRACTICAL ODWC

FRAMEWORK

3.1 The Overall Architecture

Figure 1: A generic architecture for planning-based

workﬂow composition systems. The planning ontology

and workﬂow composition engine constitute the proposed

framework and are reusable across domains and applica-

tions.

In our proposed architecture, as illustrated in Fig-

ure 1, a workﬂow composition system would utilize

three different ontologies to come up with its work-

ﬂows.

A PRACTICAL ONTOLOGY-DRIVEN WORKFLOW COMPOSITION FRAMEWORK

245

First, an Objectives Ontology is used to describe

all the workﬂow objectives that the system is expected

to accept as its inputs. In a software project manage-

ment ODWC system, for instance, this objective on-

tology is used to describe objectives such as “Produce

a workﬂow that would minimize bug count” or “Pro-

duce a workﬂow that would minimize time to mar-

ket”, etc.

Similarly, an Operators Ontology will be used to

describe all the compositional units from which the

workﬂow can be composed. In a data mining ODWC

system, for example, the Operator KB will contain

the descriptions for all the different data mining and

preprocessing algorithms.

Finally, all planning-related knowledge needed to

drive the planner and build the workﬂow from the in-

dividual compositional units is captured in the plan-

ning KB. This KB is described by a domain and

reusable Planning Ontology. As discussed in sec-

tion 2.2.3 above, this ontology provides all the nec-

essary language constructs needed to describe work-

ﬂow composition problems. A simpliﬁed version of

this ontology is shown in Figure 2.

Figure 2: A simpliﬁed view of the Planning Ontology show-

ing basic planning constructs such as states and actions.

It is worth noting that this architecture incorpo-

rates two of the desirable features identiﬁed in sec-

tion 2.2 above. Namely, it maintains a clear separa-

tion between planning-related knowledge and other

domain knowledge allowing the domain knowledge

to stay purpose-independent and hence more reusable

for future applications. It also allows the planning

logics to be described and modelled in an ontology

instead of directly in the underlying planning formal-

ism. As discussed earlier, this design feature allows

the framework to stay application-independent and

can be reused to build different workﬂow composi-

tion systems for different domains.

3.2 Integrating Goal-based Reasoning

into Ontology-driven Applications

As discussed in section 2.2.4 above, ontologies and

DLs do not provide native support for goal-based rea-

soning. We have taken a translational approach in

which the planning problem, once it has been de-

scribed by the ODWC system designer using the Plan-

ning Ontology, is translated into an equivalent ex-

ecutable program in HL and then executed using a

Prolog-based planning engine.

3.2.1 Model Closure

One of the standard cautions one has to take when in-

tegrating goal-based reasoning with ontological mod-

elling is the open-world vs closed-world assumptions

conﬂict. Goal-based program’s KBs are closed-world

models (a fact can be assumed to be false if it has

not been stated otherwise), while ontological mod-

els are open-world models (a fact cannot be assumed

false unless it was explicitly asserted). Currently how-

ever, this is not a practical issue in our situation – the

ODWC designer just has to picture a closed world in

his mind when describing his planning problem us-

ing the Planning Ontology. To help make it mentally

explicit for the designer however, we provide onto-

logical constructs that he can optionally insert into

the KB to logically (i.e. mentally) “close” the model

down. The statement “No other workﬂow actions are

available”, for example, when inserted into the KB,

has the effect of ﬁnalizing the list of already asserted

workﬂow actions and hence provides a mental closure

to the model.

3.2.2 Ensuring Translatability

Also, as with other translational approaches, one of

the main theoretical questions that is of importance

to our proposed approach is that of translatability –

how does one ensure that the planning problem de-

scriptions created by the ODWC system designer are

always translatable into an executable planning pro-

gram in HL? To answer this question, a few observa-

tions are in order.

First, a well-deﬁned ontology can be thought of as

a form of language – the list of concepts it provides

constitutes the vocabulary of the language, while the

roles it deﬁnes dictates the ways in which the vocab-

ulary can be combined together to form statements.

Secondly, by carefully controlling the list of concepts

and roles in the ontology, we can restrict or control

the types of statements one can express using the on-

tology.

With these observations in mind, one can see that

by being very selective and careful with the language

constructs in the Planning Ontology, we can ensure

that all possible workﬂow composition problem de-

scriptions are translatable to executable planning pro-

grams in HL. This, in fact, is the main intuition behind

our approach. Figure 3 provide a visual illustration

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

246

for this intuition.

Figure 3: The primary intuition behind our proposed ap-

proach: the Planning Ontology acts as a restrainer that helps

ensure the description of the planning problem always falls

into a sub area of Description Logics that is translatable to

an executable program in Horn Logic

3.2.3 Practical Beneﬁts

By taking a translational approach, as opposed to

a language modiﬁcation or extension approach, our

framework is able to make use of an existing and

mature theoretical framework (HL) and technologies

(Prolog) to provide seamless goal-based reasoning ca-

pability in ontology-driven applications.

Another advantage offered by our framework is

that it allows the ODWC system designer to continue

to think and work in the ontological modelling envi-

ronment that he or she is already comfortable with,

without having to learn a new language extension or

a new planning formalism.

3.3 Addressing Effectiveness Issues

3.3.1 Incorporating the System Designer’s

Heuristic Advice

Real-world workﬂow composition problems often re-

sult in highly complex planning problems that are

well beyond the capabilities of even the most ad-

vanced automated planners. Fortunately, in almost

all cases, the ODWC framework designer will have

some valuable procedural insights on how to take ad-

vantage of the problem’s structure or how to best go

about solving the problem. An effective and practical

planning-based ODWC framework, therefore, must

provide convenient facilities and mechanisms for the

designer to provide these insights to his system. In

our framework, this can be done via a mechanism

called partial programming (Reiter, 2001). The idea

is that, instead of relying solely on the planner to gen-

erate workﬂows from scratch, the workﬂow compo-

sition system would start from a partial template that

the system designer has provided. Because this tem-

plate contains all the heuristic advice from the de-

signer, computing a workﬂow from this template will

be much faster and efﬁcient than computing one from

scratch.

Using our framework, the system designer creates

these partial programs (i.e. templates) using the lan-

guage constructs provided by the Planning Ontology.

We describes some of these language constructs be-

low:

• Action Template. This concept is used to repre-

sent a subworkﬂow, and is the equivalence of a

procedure in a programming language. An action

template can contain workﬂow actions (i.e. com-

positional units) or other action templates. The

constituents of the template can be arranged in a

simple sequential order or in complex procedural

orders such as loops (while, foreach) or branch

(if..then..else).

• ChoicePoint. This concept is used to represent a

nondeterministic choice between two or more Ac-

tionTemplates (i.e. subworkﬂows). The designer

would use this construct to convey to his work-

ﬂow composition system that, instead of having to

consider all possibilities, it can narrow its choices

to just the subset of subworkﬂows speciﬁed in

the choice point. The more insight the designer

has about the workﬂow composition problem, the

more choice points he will put in the template and

the less work the system has to do.

• ArgChoicePoint. Each workﬂow action might

take one or more input arguments and an Arg-

ChoicePoint can be used to narrow down the pos-

sible argument values the system has to consider.

3.3.2 Hierarchical Composition

Our Planning Ontology also provides language con-

structs for dealing with hierarchical workﬂow compo-

sition. In particular, the ODWC system designer can

use the MacroAction concept to annotate (or wrap) a

subworkﬂow into a compositional unit, with its own

pre-conditions and effects. The workﬂow composi-

tion system can then use these macro units, along-

side with other regular units in a seamless manner, to

quickly compute high-level workﬂows satisfying the

objective. Once such a workﬂow has been success-

fully found, the macro units can be iteratively ﬂeshed

out into concrete subworkﬂows.

A PRACTICAL ONTOLOGY-DRIVEN WORKFLOW COMPOSITION FRAMEWORK

247

4 SUMMARY

In this paper, we reviewed the overall landscape

of ontology-driven workﬂow composition, and de-

scribed the four major approaches to which existing

works can be classiﬁed. We have also identiﬁed and

explained a list of features that are desirable for an

effective ODWC framework and proposed a practi-

cal framework that incorporates all these features to

provide the ODWC system designer with a practical,

effective and friendly means of building ODWC sys-

tems.

We have also provided a discussion on how impor-

tant theoretical challenges that are inherent to the task

of doing goal-based reasoning in ontology-driven ap-

plications can be practically addressed using existing

and mature technologies.

As for future works, we are working to provide

a formal proof on the translatability of workﬂow

composition problem descriptions. Additionally, are

also working to incorporate the notions of planning

cost/reward and concurrency into our planning frame-

work to allow it to produce non-linear workﬂows that

not only accomplish the give objective, but also ac-

complish it in an optimal way. Also, each workﬂow

action can have non-deterministic effects, and we are

also looking to incorporating non-determinism into

our framework.

A longer version of this paper including a case

study can be found at ontology.socs.uoguelph.ca.

REFERENCES

Bernstein, A., Provost, F., and Hill, S. (2005). Toward

intelligent assistance for a data mining process: An

ontology-based approach for cost-sensitive classiﬁca-

tion. IEEE Transactions on Knowledge and Data En-

gineering, 17:503–518.

Brachman, R. and Levesque, H. (2004). Knowledge Repre-

sentation and Reasoning. Morgan Kaufmann Publish-

ers Inc., San Francisco, CA, USA.

Diamantini, C., Potena, D., and Storti, E. (2009). Ontology-

driven kdd process composition. In Proceedings

of the 8th International Symposium on Intelligent

Data Analysis: Advances in Intelligent Data Analy-

sis VIII, IDA ’09, pages 285–296, Berlin, Heidelberg.

Springer-Verlag.

Gibson, A. and Stevens, R. (2009). Introduction to ontolo-

gies. In Popescu, M. and Xu, D., editors, Data Min-

ing Applications Using Ontologies in Biomedicine.

Artech Hous.

Gil, Y., Gil, A., and Blythe, J. (2000). PLANET: A Share-

able and Reusable Ontology for Representing Plans.

In Proceedings of the AAAI Workshop on Representa-

tional Issues.

Grosof, B. N., Horrocks, I., Volz, R., and Decker, S. (2003).

Description logic programs: combining logic pro-

grams with description logic. In WWW ’03: Proceed-

ings of the 12th International Conference on World

Wide Web, pages 48–57, New York, NY, USA. ACM.

Hitzler, P. and Parsia, B. (2009). Ontologies and rules.

In Bernus, P., Blazewics, J., Schmidt, G., Shaw,

M., Staab, S., and Studer, R., editors, Handbook on

Ontologies, International Handbooks on Information

Systems, pages 111–132. Springer Berlin Heidelberg.

Hlomani, H. and Stacey, D. (2009). An ontology-driven ap-

proach to software system composition. In Proceed-

ings of KEOD 2009.

Horrocks, I. (2008). Ontologies and the Semantic Web.

Commun. ACM, 51(12):58–67.

Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S.,

Grosof, B., and Dean, M. (2004). SWRL: A Semantic

Web Rule Language Combining OWL and RuleML.

Technical report, World Wide Web Consortium.

Morik, K. and Scholz, M. (2003). The miningmart approach

to knowledge discovery in databases. In In Ning

Zhong and Jiming Liu, editors, Intelligent Technolo-

gies for Information Analysis, pages 47–65. Springer.

Rajpathak, D. and Motta, E. (2004). An Ontological For-

malization of the Planning Task. In Proceedings In-

ternational Conference on Formal Ontologies in In-

formation Systems (FOIS’04), Torino, Italy.

Reiter, R. (2001). Knowledge in Action: Logical Founda-

tions for Specifying and Implementing Dynamical Sys-

tems. The MIT Press, Massachusetts, MA, illustrated

edition edition.

Russell, S. J. and Norvig, P. (2002). Artiﬁcial Intelligence:

A Modern Approach (2nd Edition). Prentice Hall.

akov

a, M., K

remen, P.,

Zelezn

y, F., and Lavra

c, N.

(2008). Using ontological reasoning and planning

for data mining workﬂow composition. In SoKD:

ECML/PKDD 2008 Workshop on Third Generation

Data Mining: Towards Service-Oriented Knowledge

Discovery.

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

248