Templates for Supporting Conceptual Modeling: a Delicate Balance

Chiara Ghidini, Muhammad Tahir Khan, and Chiara Di Francescomarino

FBK-IRST, Via Sommarive 18, 38050 Trento, Italy.|ghidini|tahirkhan|dfmchiara|@fbk.eu

Abstract. Using templates for conceptual modeling is becoming a fashionable way to guide and facilitateDomain Experts in providing rich and good quality knowledge. A possibility to build templates without startingfrom scratch is grounding them on existing foundational and core ontologies. In this paper we investigate howthese ontologies can be effectively used for the construction of templates able to guarantee a balance betweenusability and rigorousness. We report findings and lesson learned from a survey carried out for the evaluationof templates built from existing foundational and core ontologies in the enterprise domain.

1 Introduction

Effectively involving domain experts in the process of authoring OWL ontologies, making them able not only toprovide domain knowledge to knowledge engineers but also to directly write ontologies on their own or togetherwith knowledge engineers, is increasingly recognised in a number of works (see e.g., [7, 2, 22]) as a crucial stepto make the construction of ontologies more agile and apt to the needs of organisations and business enterprises.

In spite of the recognition of the problem, and of the efforts and progresses made in the area of knowledgeand ontology modelling, involving domain experts in the process of authoring OWL ontologies that provide anappropriate “computational representation of the structure, activities, processes, information, resources, people,behavior, goals, and constraints” [17] of their enterprise is still a challenging task and several efforts in the knowl-edge engineering field are currently devoted to study how to guide and facilitate domain experts in authoring OWLontologies, even if they lack a deep understanding of knowledge engineering techniques and formal languages suchas OWL.

A popular way of involving domain experts in authoring conceptual models is to provide them with graphicallanguages (diagrammatic representations). Nevertheless, as argued in [15] and briefly summarised in Section 2,diagrammatic representations are often adequate to represent only part of the knowledge that needs to be containedin a conceptual model. Templates, usually constituted by tables, forms, controlled language sentence patterns orany other kind of structured textual information, have been proposed in literature as an effective way of collectingthe rich and diversified knowledge that needs to be contained in conceptual models and to complement the onecontained in the graphical part.

While most of the analysis contained in [15] refers to software engineering conceptual modelling languages,such as UML, ER, ORM and Data flow, a similar analysis applies also to ontology based languages such asOWL. In fact, diagrammatic representations are mainly used to represent the taxonomic and membership structureof an ontology (especially these structures involve a limited number of concepts and instances) and to makethem easily understandable to ontology authors, but are not used to render more complex knowledge such asaxioms and properties of relations. Template-based approaches can therefore be applied to “encapsulate knowledgeand modelling recurrent sets of axioms” in ontology engineering, as explicitly proposed in [20] and indeed theyhave been used to better involve domain experts in authoring the entire content of specific domain ontologies inexisting tools such as MoKi [12], COE [13] and Populous [14] where templates are used to complement graphicallanguages and guide domain experts to describe ontology elements in a structured textual fashion. Templates,however, are difficult to build. They have to balance the needs of both domain experts and knowledge engineers,i.e., being usable but also rigorous enough. Moreover, methodologies for the definition of usable and rigoroustemplates need to be put in place, in order to facilitate their adoption and the customisation of tools that make useof them to specific scenarios.

In this report we propose an approach along with a concrete method to build ontology templates based on amixture of foundational, mid-level and specific ontologies, and we evaluate it through a survey conducted withdomain experts and knowledge engineers. The reason to ground templates on existing ontologies, instead of build-ing them from scratch, is that of exploiting the modelling patterns of several key entities of a domain alreadyencapsulated in well crafted ontologies, and of making them available to domain experts to reuse and adapt.

More in detail we investigate the ease of understanding, usefulness, completeness and correctness of tem-plates for modelling key entities of an enterprise ontology based on 23 foundational and core (including mid-leveland domain-specific) ontologies. The main findings of our analysis are the following: (i) grounding templates onexisting ontologies allows the construction of templates usable and rigorous enough; (ii) opportunely groundingtemplates on foundational or mid-level ontologies and characterising them with specialised domain ontologiesallows us to reach the right level of granularity of templates, thus making them more usable and precisely charac-terised; (iii) domain experts perceive template usability more positively than knowledge engineers, thus validatingthe key role of templates in supporting Domain Experts.

To the best of our knowledge, the evaluation performed in this work provides a first empirically rigorousevaluation of the support provided by a wide range of existing ontologies to the definition of structured forms thatcan guide domain experts in ontology modelling and therefore highlight, in a comprehensive manner, the potentialand criticality of using existing ontologies as a base for template-based approaches.

2 Template-based Conceptual Modeling

As extensively described in [15], template-based approaches have been used as an effective method to elicit knowl-edge in the area of software engineering and database in the last 40 years. By templates, we refer here to any kindof structured textual information (such as tables, forms, controlled language sentence patterns) where the mean-ing of the textual information contained in the template can be determined according to its position within thestructure. The first examples where tabular representations for the description of software functions [21], andglossaries for a structured description of terms relevant for a domain used in database design methodologies suchas DATAID [1] and more recently in the KCPM (Klagenfurt Conceptual Predesign Model) template-based mod-eling language [25]. With the advent of object-oriented analysis and design, and of popular graphical modelinglanguages for the representation of use cases such as UML (Unified modeling language), templates remained aneffective way to complement the graphical use case description with detailed information, which usually consti-tutes the biggest portion of knowledge of a conceptual model as graphically represented in Figure 11, as describedin [6] and in more recent approaches such as the NDT methodology [8].

The visible: a graphical representation of knowledge. Examples: classes, associations, attributes, IS-A relation, ...

The hidden: non graphically represented knowledge. Examples: descriptions, pre/post-conditions, exceptions, complex axioms, ...

Fig. 1: The iceberg of knowledge in conceptual models.

In the last few years several works have started to propose templates also to support Ontology Engineeringactivities in order to encapsulate complexity and to better involve domain experts in the activity of ontologyauthoring[20, 12–14]. Among the main advantages of introducing templates in Ontology Engineering listed inliterature are: (i) a decrease in complexity, as templates hide the complexity of the axioms and expose only therequired parameters to be filled in; (ii) an increase in efficiency of modelling, as templates can be reused in the

1 Taken from [15]

2

Fig. 2: A MoKi page.

modelling of several ontologies; and (iii) an increase in reliability, as templates comprise set of axioms developedby domain experts who, therefore become place domain knowledge at the heart of the modeling process.

2.1 Templates in MoKi

MoKi2 is a collaborative MediaWiki-based [9] tool where heterogeneous teams of knowledge engineers and do-main experts, with different knowledge engineering skills, can actively collaborate to the modelling of ontologicaland procedural knowledge in an integrated manner3. MoKi is grounded on three main pillars, which we brieflyillustrate with the help of Figure 2:

– Similarly to most state of the art wiki based tools, MoKi associates a wiki page to each each basic entity of theontology (i.e., concepts, object and datatype properties, and individuals). For instance, the concept Mountainin Figure 2 is associated to a wiki page which contains its description;

– each wiki page describes the entity it is associated with by means of both unstructured (e.g., free text, images)and structured (e.g. OWL axioms) content. This to store both the rich textual and image-based description ofinformal content and the formal description in the same tool.

– a multi-mode access to the page content is provided to support both domain experts and knowledge engineersin the authoring process. In particular, MoKi provides two modes to access the structured formal content: afully-structured access mode, where knowledge engineers can edit the ontology content by means of OWLaxioms, and a lightly-structured access mode, where domain experts, can edit the ontology content by meansof a simplified, template-based, view on the (same) structured formal description.

2 A comprehensive description of MoKi is presented in [11, 5]. The interested reader can also refer to http://moki.fbk.eu

3 Though MoKi allows to model both ontological and procedural knowledge, here we will limit our description only to thefeatures for building ontologies.

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As argued in [11], one of the advantages of MoKi, and of MediaWiki-based tools in general, is the ability ofeasily customising their form-based user interface. In fact, while the lightly-structured access mode of Figure 2represents the rather general template included in the current version of MoKi, specific customisations of MoKicontain ad hoc templates for the description of different entities.

Data Properties

Full Name

Location

Start Date

End Date

Event type

Event description

Event

Every Event has partecipant

Every Event is hosted by

Every Event is organized by

Every Event is associated to

Object PropertiesOrganization/People

Organization/People

Organization/People

Process

Is aEvery Event is a

Fig. 3: A template.

However, defining ad hoc templates such as the one drafted in Figure 3, which contains a structured descriptionof key domain knowledge, that is the concept “Event”, and can be used by domain experts to: (i) refine / adapt thecharacterisation of the concept to the specific domain; (ii) model more specific concepts (e.g., a concept “Productlaunch event”) in a guided manner; and (iii) populate the ontology with specific instances of the concept (e.g., theinstance “Fiat 500 UK launch event”) is a difficult and time consuming task, as templates need to be produced bybalancing usability aspects and rigorousness aspects.

To address this problem we have investigated an approach which builds templates based on a mix of founda-tional, mid-level and domain-specific ontologies, which we present in Section 3 and evaluate in Section 4. Notethat while the work presented in the paper is motivated by the work on MoKi, as described in the previous section,the methodology presented in Section 3 and the evaluation presented in Section 4 have a general character and donot depend upon the tool used. Therefore they can provide a first insight on the potential and problems of usingexisting ontologies to structure templates for ontology engineering.

3 Building Templates for Enterprise Ontologies

In this section we provide an overview of the approach we followed to construct templates for the domain ofEnterprise Ontologies. We selected the domain of Enterprise Ontologies as this provides an important and generaldomain where domain experts are usually involved, but the suggested mehtod that we have followed can providesuitable guidelines for the definition of templates to support the task of ontology engineering in a wide range ofspecific domains.

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The approach The approach relies on building the templates based on a model extracted from a range of existingontologies, which include foundational, mid-level and domain-specific ontologies. The reason to ground templateson existing ontologies, instead of building them from scratch, is that of exploiting the ontological analysis per-formed and modelling patterns already encapsulated in well crafted ontologies, and of making them available todomain experts to reuse and adapt. The reason to exploit a number of existing ontologies, ranging from foun-dational to mid-level, to specific ones is to evaluate the appropriateness of the different resources to the specifictask.

The Realisation The method that we have followed is composed of the four macro steps illustrated in the remainingof the section.

As a first step we focused on selecting entities relevant to our problem domain from foundational ontologies.The reason to start from foundational ontologies is that they contain a well founded characterisation of the foun-dational ontological categories, and therefore provide a good a starting point for building good quality ontologies.In our case we decided to use the DOLCE ontology [18], which, we believe, provides a good reference ontologyfor defining templates on a wide range of domains which encompass the one investigated in our experiment.

The second step concerned in complementing / specifying the entities selected from the DOLCE ontologywith entities selected from core (mid-level) ontologies. In fact, while grounding the templates on DOLCE helpsus to build templates that provide a rigorous characterisation of the entities they have to describe, we need toproduce templates that guide domain experts in the modelling of specific entities for a given domain. Mid-levelontologies provide the best candidate for this task as they usually describe specific (yet general) concepts for aparticular domain (in our case the enterprise domain). Our strategy was first to review mid-level ontologies thatare produced either as extensions of DOLCE (see e.g., the Ontology of Social roles presented in [19]) or groundedon DOLCE (see e.g., [3]) and to select entities relevant to our domain together with their characterisation.

The third step was to put together the entities selected in the first two steps in a taxonomy of 20 entities,asillustrated in Figure 4. The leaves of this taxonomy constitute the 8 entities to be characterised in the templatesthat we have evaluated in our experiment, shown in Figure 5 . Note that while reviewing the mid-level ontologieswe found out that some of the entities we were interested in are contained both in foundational and mid-levelontologies. In our experiment that was the case for “event” and “process”. In Figure 4 we have decided to depictthe entities in the most abstract type of ontology in which they were found. Thus “event” and “process” are listedamong the entities coming from foundational ontologies. We also found out that several entities are contained inmore than one mid-level ontology. In these cases we decided to merge the characterisation of the entities containedin the different ontologies to produce a more comprehensive one, and possibly to solve some conflicting issues,which were nevertheless extremely limited in our experience.

The final step concerned in evaluating the completeness of the characterisation of the different entities. Evalu-ating completeness is a complex task. Therefore we decided to take a pragmatic approach and aim at a characteri-sation composed of a number of properties ranging between 5 and 20. This, in order to have enough “attributes” todescribe the entity, but not too many to become a burden for the domain experts. To complement the descriptionof the entities we looked at domain-specific ontologies such as The Enterprise Ontology [23] and Schema.org4.The whole process resulted in an ontology of 20 concepts, 86 relations and 159 axioms which has provided thebasis for the construction of the templates for Organization, Actor, Role, Process, Event, Task, and Artefact. Indetail, the ontology Description Logic axioms have been used for the automatic construction of the templates: Fig-ure 3 shows an example of the resulting Event template. A description of the characterisation of the seven entities,together with the documentation of the source ontologies that have been taken from and the final templates aredescribed in Appendix.

4 Survey Evaluation

We are interested in investigating whether templates built starting from existing ontologies can be used to supportdomain experts in the conceptual modeling while preserving the rigorousness and the accuracy characterizing thegood principles of knowledge engineering. These templates should indeed be able to fill the gap between DomainExperts and Knowledge Engineers while balancing usability and ability to capture the design needs. In order toevaluate whether they can suit the different requirements they are supposed to satisfy, we built (Section 3) a set oftemplates grounded on different existing ontologies ranging from foundational to very specific domain ontologiesand related to the enterprise domain, evaluating them through a survey.

4 http://schema.org

5

Fig. 4: A case for Enterprise Model.

Fig. 5: A case for Enterprise Model: Selected entities for Templates.

4.1 Survey Description

The goal of the conducted survey is investigating (i) the usability (in terms of ease of use and usefulness) oftemplates built starting from existing ontologies as perceived by both Domain Experts and Knowledge Engi-neers; and (ii) the capability of these templates to completely and precisely capture the modeling needs of thespecific domain as perceived by both Domain Experts and Knowledge Engineers. The research questions weare interested to evaluate are:

RQ1 Are templates built from existing ontologies perceived as usable by Domain Experts and KnowledgeEngineers?

RQ2 Are templates built from existing ontologies able to completely and precisely satisfy the modeling needs ofa specific domain according to Domain Experts and Knowledge Engineers?

RQ1 aims at investigating the usability of templates by their final users, i.e. Domain Experts and KnowledgeEngineers. RQ2, instead, evaluates the modeling capabilities offered by the templates through their characteri-zations.

In order to answer the above research questions, we implemented the seven templates described in Section 3in MoKi and we asked a group of experts including both Domain Experts and Knowledge Engineers to answer

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a questionnaire investigating four main factors: (i) ease of understanding; (ii) usefulness; (iii) characterizationcompleteness and (iv) characterization precision for each template. We used the subjective perception expressed bysubjects about the first two factors for investigating whether templates built using well known existing ontologicalresources are actually usable (i.e., for answering RQ1) and the last two factors for providing a subjective evaluationon the feasibility of the usage of ontological resources for addressing the real needs of the users in terms ofcharacterizations (RQ2).

The subjects involved in the survey were 43 among Domain Experts and Knowledge Engineers. DomainExperts are mainly enterprise workers with, on average, high knowledge of enterprises and medium expertisein modeling. Knowledge Engineers are instead both modeling experts and ontologists with average modelingexpertise almost high. We asked each of them to evaluate a set of three (TA) or four (TB) templates. In detail 21subjects (11 Knowledge Engineers and 10 Domain Experts) were asked to evaluate the TA templates (Role,Artefact and Actor), while the remaining 22 subjects (13 Knowledge Engineers and 9 Domain Experts) toevaluate the TB templates (Organization, Task, Event and Process). Table 1 reports the distribution of enterprisetemplates among the different experts.

TA TB

Knowledge Engineers 11 13Domain Experts 10 9

Table 1: Distribution of the enterprise templates among Domain Experts and Knowledge Engineers

Each template has been evaluated by means of a set of questions belonging to two categories: (i) closed-endedquestions, where answers were selected from a 5 point Likert-scale, (ii) open-ended questions, where participantswere allowed to provide suggestions for improving the templates. In detail, we used closed-ended questions and 5point scales for asking experts their subjective perception about template ease of understanding (0=immediate, ...,2=reasonable, ..., 4=complex), usefulness (0=absolutely useful, ..., 2=neither useful nor useless, ..., 4=absolutelyuseless) and completeness (0=totally complete, ..., 2=neither complete nor incomplete, ..., 4=totally incomplete).For the precision, instead, subjects were asked to evaluate, for each template characterization whether they wouldkeep or discard it (yes/no answer). For each template, we used the collected answers to compute, according to theinformation retrieval notion of precision, the subjective precision of each expert, i.e., p(si) = |ACT (si)|

|CT | , whereCT is the set of the provided characterizations for the template T , and ACT (si) ⊆ CT the subset of CT positivelyjudged by the expert si. We then classified the values of each subjective precision in three categories: high (100%precision, i.e., the subject agreed on all the provided template characterizations), reasonable (the precision isbetween 66% and 100%, i.e., more than 2/3 of the characterizations have been judged correct), and low (less than66% of precision). Templates have been made available to subjects on-line and questions provided in the form ofan on-line survey.

4.2 Survey Results

In this subsection we report the results of our observations and the statistical analyses we applied to strengthenthese results. In detail, to assess the positivity of the experts’ evaluations, since values are derived from an ordinalscale, we test medians using a one-tailed Mann-Whitney test [26], verifying the hypothesis that F ≤ 2, whereF can be each of the four factors under investigation, F is its median and 2 the intermediate value of the scale.Moreover, in order to evaluate the magnitude of the statistical significance obtained, we computed the effect size,that provides a measure of the strength of the relationship between two variables, by means of the Cohen’s dformula. Finally, we used a one-tailed Mann-Whitney test [26] to compare pairs of templates with respect tothe same factor, thus identifying possible templates performing worse or best with respect to the others. All theanalyses are realized with a level of confidence of 95% (p-value < 0.05), i.e., there is only a 5% of probabilitythat the results are obtained by chance.

Table 2 reports the descriptive statistics (median value and percentage distribution) related to the answersprovided by subjects for RQ1 and RQ2. For the sake of readability, in reporting the percentage distribution, wegroup together the two positive and the two negative values of the three 5 point scale, thus reporting the percentageson a 3 point scale, as for the subjective precision (high, reasonable and low).

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By looking at the table, it comes out that subjects perceived the templates as overall easy to understand (themedian value is 1, i.e. “easy” on the 5 point scale). Such a overall positive evaluation is also confirmed at statisticallevel (p-value < 0.05) and by the large effect size5. Indeed, by looking at the distribution of the provided answers,more than half of them state the ease understanding of templates, while in only 11% of the cases the tempmlateshave been evaluated difficult to comprehend. Nevertheless, the number of answers that are clearly positive ornegative is not trivial (about 33%). Although reasonable could also be considered a positive evaluation, in theset reasonable answers also fall the ones of experts that did not want to risk taking a clear stand. Consideringthe distribution of these “answers in the middle” per template, we found that the evaluation provided for theRole template are significantly worse (at statistical level) than those provided for the Task, Event and Processtemplates, thus remarking the criticality of this template. By looking at Figure 6 (left), showing the subjectiveevaluation related to ease of understanding provided by the experts for each template, the criticality of the Roletemplate, for example, is quite evident: 62.5% of the subjects expressed a borderline answer for this template. Thehigh number of reasonable answers, hence, can be partially reconducted to this case. On the contrary, the Eventtemplate is the easiest to understand resulting at statistical level easier to understand not only than the Role butalso than the Artifact one. subjects provided an overall positive evaluation also with respect to the usefulness.

Moreover, by further investigating the figure and comparing pairs of template evaluations (related to the easeof understanding), we found that the evaluation provided for the Role template are significantly worse at statisticallevel) than those provided for the Task, Event and Process templates, thus remarking the criticality of this template.

On the contrary, the Event template looks the easiest to understand, resulting at statistical level easier tocomprehend not only of the Role but also the the Artefact one.

RQ Factor Median p-value cohen-d Rate Percentage

RQ1

Ease of Understanding 1 < 0.05 0.87easy 55.3%

reasonable 33.3%difficult 11.4%

Usefulness 1 < 0.05 0.82useful 64.9%

neither useful nor useless 20.2%useless 14.9%

RQ2

Completeness 1 < 0.05 0.6complete 56.1%

neither complete nor incomplete 25.4%incomplete 18.4%

Precision 1 < 0.05 2.43high 82.5%

reasonable 14.9%low 2.6%

Table 2: Statistical measure of the survey results

Subjects provided an overall positive evaluation also with respect to the usefulness. The median value of theevaluation on the 5 point scale is indeed 1, i.e., useful. The result is also statistically confirmed by the Mann-Whitney test and, also in this case, the effect size is high. Templates have been judged overall useful in almost65% of the cases versus the 15% of useless evaluations. In this case, the borderline answers were slightly less(about 20%) than for the ease of understanding, still representing a non-trivial percentage. One of the reasons forthese uncertain answers could be the lack of usage of the templates in a practical setting: subjects were providedwith templates and questionnaire without being required to perform any practical task. This way, unless they hadalready been exposed to similar situations, getting a clear idea of the usefulness of the templates, could havebeen difficult. This explanation is in part confirmed by looking at the distribution of the unclear stands betweenKnowledge Engineers and Domain Experts. Almost 96% of neither useful nor useless answers have beenprovided by Knowledge Engineers. Also in this case, the evaluation provided for the Role template are worsethan the others. Indeed, also at statistical level the usefulness of the Role template has been perceived as worsethan the one of Actor, Task, Event and Process.

We can hence conclude that, except for some critical templates as the Role one, usable templates can bebuilt starting from existing ontologies and hence positively answer to RQ1. Nevertheless, to have a more preciseevaluation, mainly of the usefulness of these templates, subjects should had the possibility to use them in practicaltasks.

The third row of Table 2, reports median, statistical analyses and distribution of the survey answers related tothe characterization completeness. Experts evaluated the characterizations as overall complete: the median value

5 The effect size is considered to be small for 0.2 ≤ d < 0.5 medium for 0.5 ≤ d < 0.8 and large for d ≥ 0.8

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Fig. 6: Template usability subjective evaluation

is 1, i.e., complete in the 5 point scale, with ˜Compl < 2 (p-value < 0.05); the cohen-d effect size, instead, islower than for the other factors (medium). Indeed, in this case, although more than half of the evaluations werepositive, the percentage of the negative ones is slightly more than for the other factors (18%). Similarly to theusefulness evaluation, most of the 25% of uncertain answers (79.3%) was provided by Knowledge Engineers,who are overall more rigorous about what a complete characterization is. By looking at the figure, the Organizationtemplate seems to be the most problematic one. Indeed, the subjects’ answers related to its completeness result tobe (at statistical level)worse than those of Artefact, Actor and Event, while no other significant differences existamong the other templates.

Finally, the fourth row of Table 2 reports the values related to the precision of the template characterization.Overall the precision is high: the median is 1, i.e., the highest value and P ≤ 2 (with p-value ≤ 0.05). Indeedmost of the evaluations (82%) are high, while only a small percentage(2.6%) is negative. Again, when looking atthe distribution of the subjects’ evaluation on the different templates we found that there are some differences. Forexample the Event and the Process templates outperform the others, while the Role and Actor characterizationsare less precise. The across-template statistical test also confirms this result indicating a worse performance of theRole and Actor templates with respect to the Event and the Process ones.

Overall we can hence positively answer RQ2, although the characterization completeness and precision is notthe same for all the templates.

4.3 Discussion

The analyses of the above research questions revealed that differences exist in the perception of Domain Expertsand Knowledge Engineers, as well as in the evaluations they provide for the different templates.

In relation to the difference between Domain Experts’ and Knowledge Engineers’ perception, Figure 8reports their evaluations for each of the four factors. Surprisingly, by looking at the figure, it seems that DomainExperts judged template ease of understanding, usefulness and characterization precision more positively thanKnowledge Engineers. On the contrary, the completeness of the template characterization has been evaluatedslightly more positively by Knowledge Engineers. This result can be partially explained by considering thedifferent competencies and objectives guiding the two roles.

Domain Experts, indeed, are interested in the practical issues related to the domain they need to represent.They want a template that is useful to describe exactly what they need to describe, without caring whether termscould convey other meanings in other contexts. On the contrary, Knowledge Engineers are more focused on

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Fig. 7: Subjective evaluation of the template characterizations completeness and precision

the formal part: to be easy to understand a term should be enough detailed and clear so as not to confuse itssemantics; applying the same criteria to the properties of the template characterizations, their perception of thecharacterization precision decreases as well.

Regarding the differences among templates, instead, in order to investigate the reasons behind the criticalitiescharacterizing some of them, we looked more in detail at how they have been built and to the ontologies they havebeen grounded to. Overall, two out of the seven templates are defined not only in domain-specific (DS) and mid-level (ML) ontologies, but also in foundational (F) ontologies and, among them, there is the one (Event) that isbetter perceived with respect to almost all the factors. Table 3 reports for each template, the typology of ontologyfrom which the corresponding entity has been taken as well as the percentage of characgterization attributes andproperties belonging to foundational, mid-level or domain specific ontologies, respectively. Finally it also reportsthe percentage of attributes and properties used to complete the characterization, as well as the percentage ofproperties common to more than two ontologies.

Template Entity Foundational Mid-level Domain Other shared by > 2Ontology specific ontologies

Role ML and DS 0 93.8% 0 6.3% 37.5%Artefact ML and DS 0 75% 8.3 % 16.7% 14.3%Actor ML and DS 23.5% 64.7% 0 11.8% 0

Organization ML and DS 10 % 40% 30% 20% 0Task ML and DS 0 40% 40% 20% 0Event F, ML and DS 7.7% 15.4% 46.2% 30.8% 0

Process F, ML and DS 8.3% 58.3% 25% 8.3% 0

Table 3: Distribution of the enterprise templates among Domain Experts and Knowledge Engineers

The table reveals that differences exist in the template composition (in terms of ontology categories used fortheir construction). Crossing these data with the users’ evaluations we found that the largest part of the characteri-zation of the templates that have been perceived as easier to understand and more precisely defined than the others(i.e., the Event and the Task templates) comes from specific ontologies. This result is also confirmed at statisticallevel. Indeed, we found a strong positive correlation 6 between the percentage of characterizations deriving from

6 We performed a correlation analysis applying the Pearson’s coefficient at 5 percent confidence level.

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Fig. 8: Domain Experts and Knowledge Engineers evaluations

domain specific ontologies and the average evaluation of both ease of understanding and characterization preci-sion. In other terms, the higher the percentage of specific properties is and the higher the perception of the ease ofunderstanding and of the precision is. On the contrary, a negative correlation exists between the percentage of at-tributes and properties from mid-level ontologies and the average perception of ease of understanding, usefulnessand characterization precision

A second factor that seem to influence the perception of both ease of understanding and usefulness is thenumber of different source ontologies sharing the same property or attribute. Surprisingly, characterizations sharedby more than two ontologies seem to negatively affect the overall perception of the ease of understanding andusefulness of the template. This result can be connected, as well, to the negative correlation between too generalcharacterizations and their usability.

Hence, a good trade off to build templates able to find the right balance between rigorousness and usabilityand satisfy both the needs of Domain Experts and Knowledge Engineers, seems to be starting from founda-tional ontologies concepts and, passing through mid-level ontologies, characterizing the templates with concreteproperties obtained exploiting domain specific ontologies.

4.4 Threats to validity

Despite the rigorousness and the care used for conducting the survey and for analyzing the results some threatscan hinder the result validity. A first threat is due to the lack of the usage of the templates in a real-life task.Nevertheless, the templates were accompanied with a description of a possible scenario, thus helping the subjectsto figure out a possible practical use case for the template usage. A second threat is the subjectivity that has beenused for building the templates. The impact of this threat, however, is partially limited by the definition and theusage of a methodology the construction of the templates. Finally, the specificity of the domain considered is afurther threat to the validity of the study. On the other hand, the enterprise domain is a quite general domain inwhich Domain Experts are often involved.

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5 Related Work

The use of templates is recently becoming an attractive means to hinder complexity and support domain experts inthe ontology authoring. Works dealing with templates based approaches in the ontology engineering field can bemainly classified in two categories: those focused on the ontology structure and those oriented to both structuraland domain knowledge.

There has been some effort done to generate templates. As in [20], authors presents a approach to supportontology engineers in modelling of ontology templates. They have demonstrated their approach with templatesfor ontology design patterns. This work is different from our approach as it is more focused toward knowledgeengineers and it needs to be extended, the usability aspect have not been studied and it has not been used for toplevel ontologies.

Also in MoKi [12] templates are mainly used for providing structural support to the ontology authoring. Thelightly-structured view, indeed, provide a semi-formal rendering of the OWL content in a predefined template tosupport domain experts access to the content of the ontology. The work of this paper will extend the tools likeMoKi with a set of templates grounded on existing foundational ontology and core ontologies instead of startingfrom scratch.

A slightly different approach is the one of Populous [14], a template based approach designed for domainexperts to gather knowledge that can be used to build ontologies. In Populous the authors advocate the use ofspreadsheets as templates to gather and organise information about concepts and their relationships as it providesa simple and intuitive form fill-in style of user interface. However, the main purpose of this approach is knowledgegathering, the population of the templates at the point of data entry, and not ontology building. In this paper, in-stead, we focus on the complexity of building usable and rigorous templates. Another approach that uses templatesto capture experts knowledge is presented in [13]. In order to acquire knowledge from experts this approach al-lows users to structure their knowledge by using concepts maps or the formalised knowledge structures defined astemplates. The templates in this approach corresponds to commonly used owl structures (e.g., subclass, instance,owl restrictions), they do not help very much in the overall structure of the ontology.

6 Conclusion

The paper shows, through a rigorous evaluation, that templates based on existing foundational and core ontologiesare able to reconcile usability and formality dimensions, ensuring ease of understanding and usefulness whileguaranteeing completeness and precision. As a side effect of our evaluation we provide some insights about therelation between the different types of ontologies used for building templates and their resulting perception byusers. We plan for the future to go ahead with this stream of work, further investigating the impact of different cat-egories of ontologies on template characteristics, thus (i) refining the proposed methodology for the constructionof ontology-based templates; and (ii) devising measures and criteria of their appropriateness to define templatesthat meet certain requirements.

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gies. In: Proc. of ISWC 2008. pp. 1–16. Berlin (2008)8. Escalona, M., Reina, A., Torres, J., Mejıas, M.: Ndt a methodology to deal with the navigation aspect at the requirements

phase. In: OOPSLA Workshop: Aspect-Oriented Requirements Engineering and Architecture Design (2004)

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9. Foundation, W.: Mediawiki. http://www.mediawiki.org (Accessed on 6 Nov 2011)10. Gangemi, A., Borgo, S., Catenacci, C., Lehmann, J.: Task taxonomies for knowledge content d07. Tech. rep., Metokis

Project (2004)11. Ghidini, C., Rospocher, M., Serafini, L.: Conceptual modeling in wikis: a reference architecture and a tool. In: The Fourth

International Conference on Information, Process, and Knowledge Management (eKNOW2012), Valencia, Spain. pp.128–135 (2012), http://www.thinkmind.org/download.php?articleid=eknow_2012_6_10_60015

12. Ghidini, C., Rospocher, M., Serafini, L.: Modeling in a wiki with moki: Reference architecture, implementation, andusages. International Journal On Advances in Life Sciences 4(3&4) (2012), to appear

13. Hayes, P., Eskridge, T.C., Saavedra, R., Reichherzer, T., Mehrotra, M., Bobrovnikoff, D.: Collaborative knowledge capturein ontologies. In: Proceedings of the 3rd international conference on Knowledge capture. pp. 99–106. K-CAP ’05, ACM,New York, NY, USA (2005), http://doi.acm.org/10.1145/1088622.1088641

14. Jupp, S., Horridge, M., Iannone, L., Klein, J., Owen, S., Schanstra, J., Wolstencroft, K., Stevens, R.: Populous:a tool for building owl ontologies from templates. BMC Bioinformatics 13(Suppl 1), S5 (2012), http://www.biomedcentral.com/1471-2105/13/S1/S5

15. Kop, C., Mayr, H.C.: Templates in domain modeling - a survey. In: The Evolution of Conceptual Modeling. pp. 21–41(2008)

16. Magro, D., Goy, A.: Towards a first ontology for customer relationship management. In: Chbeir, R., Badr, Y., Abraham,A., Laurent, D., Koppen, M., Ferri, F., Zadeh, L.A., Ohsawa, Y. (eds.) CSTST. pp. 637–643. ACM (2008)

17. Mark S. Fox and Michael Gruninger: Enterprise modeling. AI Magazine 19(3), 109–121 (1998)18. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: WonderWeb deliverable D18 ontology library (final).

Tech. rep. (2003)19. Masolo, C., Vieu, L., Bottazzi, E., Catenacci, C., Ferrario, R., Gangemi, A., Guarino, N.: Social roles and their descriptions.

In: KR. pp. 267–277 (2004)20. Parreiras, F.S., Groner, G., Walter, T., Staab, S.: A model-driven approach for using templates in owl ontologies. In:

EKAW. pp. 350–359 (2010)21. Ryszard, J., Parnas, D.L., Zucker, J.: Tabular representations in relational documents. In: Hoffman, D., Weiss, D. (eds.)

Software Fundamentials – Collected Papers by David Parnas, pp. 71–85. Addison Wesley Publishing Comp (2001)22. Tudorache, T., Falconer, S.M., Noy, N.F., Nyulas, C., Ustun, T.B., Storey, M.A.D., Musen, M.A.: Ontology development

for the masses: Creating icd-11 in webprotege. In: Cimiano, P., Pinto, H.S. (eds.) Knowledge Engineering and Managementby the Masses - 17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings. LectureNotes in Computer Science, vol. 6317, pp. 74–89. Springer (2010)

23. Uschold, M., King, M., House, R., Moralee, S., Zorgios, Y.: The enterprise ontology. The Knowledge Engi-neering Review 13, 31–89 (1998), http://reference.kfupm.edu.sa/content/e/n/the_enterprise_ontology__241225.pdf

24. Vieu, L., Borgo, S., Masolo, C.: Artefacts and roles: Modelling strategies in a multiplicative ontology. In: Proceedingof the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference(FOIS 2008). pp. 121–134. IOS Press, Amsterdam, The Netherlands, The Netherlands (2008), http://portal.acm.org/citation.cfm?id=1563953.1563967

25. Vohringer, J., Mayr, H.C.: Integration of schemas on the pre-design level using the kcpm-approach. In: Nilsson, A., Gustas,R., Wojtkowski, W., Wojtkowski, W., Wrycza, S., Zupancic, J. (eds.) Advances in Information Systems Bridging the Gapbetween Academia & Industry, pp. 623–634. Springer, Heidelberg (2006)

26. Wohlin, C., Runeson, P., Host, M., Ohlsson, M.C., Regnell, B., Wesslen, A.: Experimentation in software engineering: anintroduction. Kluwer Academic Publishers (2000)

7 Appendix A

Now, here we will provide a brief description of the template. The structure of the template is based on theontological building blocks. The subclass section represents the top level category to which concept belongs andif there is some part of relation exists then it will have a section for representing all the possible part of relations athat concept can have. Finally there is a property section to represent relation of that concept with other concepts.The tables below presents the characterization of templates along with the sources from where it is derived. Thereare columns stating the reason for modification if the property has been modified from the one existing in thesource ontology.

Organization: According to [4], Organizations are complex social entities, created and sustained by human agents.In other words organization is a complex entity linked to a group of people that are able to perform complexactivities which otherwise could not be accomplished by non coordinated individuals. Entities that populate theorganisation are: the agents who become part of organisation by accepting to play certain roles that are assigned tothem by organisation to act on its behalf, teams and departments. organisations arrange the agents into departments,teams and assign them the tasks to perfom.

13

14

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In a organisational setting there are different events that occur. Let’s suppose that an Organisation ”FIAT”arranges a Product launch event ”Fiat 500 UK launch event” at the London Eye on Monday 12-Dec-2012 and itwill end on 14-Dec-2012. The event type of this event will be a organizational event, more specifically a ProductLaunch event. In order to explain to people what it is about and attract more people to this event a description isprovided to the participants. To arrange this event successfully there is a process specified by the FIAT to followand it also designate the people who are responsible for organising event. These people play the role of Eventmanagers in the organisation. They are responsible for planing and executing this event. In this events there is aevent host to explain the functionalities of this new product and guide the participants through different stages ofthe event.

18

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Role: In an organisation a role can be played by different entities or a same entity can play different roles. Theroles are defined by the norms as they regulate the behaviour of actor playing the roles, dictating what they areallowed to do (directly or indirectly), what they are obliged to do. This means that the actor who decides to becomepart of an organisation must agree to undertake all the rights and duties connected to the role that (s)he will playwithin the organisation [4]. As mentioned by [16] roles not on themselves decide what tasks to perform but theyare assigned to them by other roles e.g., an employee can be given instruction to perform a task by its director.

Accordig to [4][19] a role can specialise another role like ’Production manager’ is a spacialisation of the role’manager’ and there is a precise hierarchy of roles , a forced path to follow in order to reach certain position orplay a certain role. It means that in order to a role the actor must have played a role that is required by the currentrole. Being a ’Production manager’ a person has certain right (e.g., request information from other roles, staff thedepartment, access organisation information and data ), fullfill different duties (e.g., compile the production plan) and carry out the assigned task (decision on production material, order materials and supplies, plan and developnew products or production processes).

21

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has

task

Task

Task

Taxo

nom

ies

forK

now

ledg

eC

onte

ntD

07

Act

orha

stas

kta

skA

ctor

sar

epl

ayin

gro

les

inth

eO

rgan

isat

ion

soth

eta

sks

are

assi

gned

toth

ose

role

sSp

ecia

lize

byR

ole

Prel

imin

arie

sto

aD

OL

CE

onto

logy

ofor

gani

satio

ns

Soci

alR

oles

and

thei

rDes

crip

tions

Rol

eSp

ecia

lize

Rol

e

requ

ires

Rol

ePr

elim

inar

ies

toa

DO

LC

Eon

tolo

gyof

orga

nisa

tions

Soci

alR

oles

and

thei

rDes

crip

tions

Rol

eR

equi

res

Rol

e

22

Artefact: Artefacts are the entities created for some purpose or use and they are the result of agent’s intentionality.They are constituted by non Agentive Physical objects or amounts of matter. Artefacts can also play roles e.g., Ahouse is an artefact which can play the role of being someones home. The key element that distinguishes productsfrom artefacts is the exchange purpose[24]. In an organisation, artefacts (Products & Documents) are producedby agents playing certain role or by the project carried out by organisation, or as a output of a some processin organisation. For example, consider the document ’Product description’ describing the product details can beconsidered as an artefact produced by an agent who is playing the role of ’technical writer’ in the organisation.

23

Art

efac

tSo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

yPr

oper

tyR

easo

nSo

urce

Ont

olog

yPr

oper

tyR

easo

n

Prop

ertie

sR

ange

full

nam

est

ring

(Ple

ase

Spec

ify)

Self

Driv

en

has

Idst

ring

(Ple

ase

Spec

ify)

Self

Driv

en

purp

ose

stri

ng(P

leas

eSp

ecif

y)

AR

TE

FAC

TS

INFO

RM

AL

ON

TOL

OG

Y

Art

efac

t—Pu

rpos

e—(R

ange

notd

efine

d)

Con

side

rthe

agen

t’s

inte

ntio

nsun

derl

ying

the

crea

tion

ofan

arte

fact

.The

rear

etw

oas

pect

s:th

ein

tent

ion

toge

tan

entit

yfo

rso

me

purp

ose

orus

e,an

dth

ein

tent

ion

toph

ysic

ally

mod

ify

orpr

oces

sso

me

pree

xist

ing

entit

yto

“pro

duce

”th

ear

tefa

ctha

sde

scri

ptio

nst

ring

(Ple

ase

Spec

ify)

Beh

avio

rofa

Tech

nica

lArt

ifact

:A

nO

ntol

ogic

alPe

rspe

ctiv

ein

Eng

inee

ring

has

desc

ript

ion—

beha

vior

alan

dfu

nctio

nal

desc

ript

ions

has

role

Rol

eA

rtef

acts

and

Rol

es:M

odel

ling

Stra

tegi

esin

aM

ultip

licat

ive

Ont

olog

y

From

Phys

ical

Art

efac

tsto

Prod

ucts

AR

TE

FAC

TS

INFO

RM

AL

ON

TOL

OG

YPl

ays

the–

Rol

e

deve

lope

dby

Pers

on/

Org

aniz

atio

nFr

omPh

ysic

alA

rtef

acts

toPr

oduc

ts

AR

TE

FAC

TS

INFO

RM

AL

ON

TOL

OG

Y

crea

tedb

y–ag

ento

rpr

oces

sT

hede

finiti

onof

Age

ntal

soin

clud

esso

cial

agen

ts;

Org

aniz

atio

npr

oduc

edby

Proj

ect/

Proc

ess

The

Con

cept

of‘P

roje

ct’:

APr

opos

alfo

raU

nify

ing

Defi

nitio

n

From

Phys

ical

Art

efac

tsto

Prod

ucts

Prod

uce

duri

ng–

Proj

ect

Art

ifact

sdo

cum

entin

gag

reem

ents

ina

proj

ect:

Req

uire

men

tspe

cific

atio

n,pr

ojec

tpla

n,pr

oduc

tar

chite

ctur

e,m

inut

esfr

omst

eeri

ngco

mm

ittee

mee

ting,

crea

tedb

y–ag

ento

rpr

oces

s

24

Actor: Actors are the physical agents in DOLCE ontology having some affiliation to an organization by playingsome role in it . They are the owners of some processes and perform activities designated to them.

25

Act

orSo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

yPr

oper

ty

Rea

son

Sour

ceO

ntol

ogy

Prop

erty

Rea

son

Prop

ertie

sR

ange

full

nam

est

ring

(Ple

ase

Spec

ify)

Self

Driv

en

acto

rid

stri

ng(P

leas

eSp

ecif

y)

Self

Driv

en

invo

lved

inPr

ojec

tT

heC

once

ptof

‘Pro

ject

’:A

Prop

osal

fora

Uni

fyin

gD

efini

tion

peop

leIn

volv

edin

Proj

ect

peop

leco

rres

pond

tope

rson

and

pers

onar

eA

ctor

sin

our

dom

ain

Dep

artm

ent

hasm

embe

rro

les

Rol

esar

epl

ayed

byA

gent

s.So

we

reco

rdth

ein

form

atio

nat

the

leve

lofA

gent

sno

tatR

oles

mem

bero

fTe

am/

Dep

artm

ent

Prel

imin

arie

sto

aD

OL

CE

onto

logy

ofor

gani

satio

ns

AFo

unda

tiona

lO

ntol

ogy

ofO

rgan

izat

ions

and

Rol

es

hasm

embe

r-ag

ent

depe

ndon

the

role

(s)p

laye

dby

thei

rmem

bers

has

role

Rol

ePr

elim

inar

ies

toa

DO

LC

Eon

tolo

gyof

orga

nisa

tions

AFo

unda

tiona

lO

ntol

ogy

ofO

rgan

izat

ions

and

Rol

es

Age

ntpl

ays

role

affil

iate

dO

rgan

izat

ion

Prel

imin

arie

sto

aD

OL

CE

onto

logy

ofor

gani

satio

ns

Age

ntiv

eph

ysic

alO

bjec

t—A

ffilia

ted—

Org

Act

oris

aA

gent

ive

obje

ct

resp

onsi

ble

for

Proc

ess

The

ente

rpri

seon

tolo

gyA

ctor

resp

onsi

blef

orA

CT

IVIT

Y

We

cons

ider

Act

ivity

asa

Proc

ess

actf

orO

rgan

izat

ion

Prel

imin

arie

sto

aD

OL

CE

onto

logy

ofor

gani

satio

ns

phys

ical

agen

tac

tsfo

rorg

exec

ute

Serv

ice/

Task

Tow

ards

anO

ntol

ogic

alFo

unda

tion

for

Serv

ices

Scie

nce

Task

Taxo

nom

ies

forK

now

ledg

eC

onte

ntD

07

Age

ntex

ecut

esse

rvic

ean

agen

tis

expl

icitl

yco

mm

itted

togu

aran

tee

the

exec

utio

nof

som

ety

peof

actio

n

Act

orha

stas

kta

sk

26

Act

orSo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

ySo

urce

Ont

olog

yPr

oper

ty

Rea

son

Sour

ceO

ntol

ogy

Prop

erty

Rea

son

part

icip

ate

Eve

ntD

OL

CE

D18

End

uran

tPa

rtic

ipat

ein

Perd

uran

t

Act

oris

anE

ndur

anta

ndE

vent

isa

Perd

uran

t

part

icip

ate

Proc

ess

DO

LC

ED

18E

ndur

ant

Part

icip

atei

nPe

rdur

ant

Act

oris

anE

ndur

anta

ndE

vent

isa

Perd

uran

t

prod

uce

Art

efac

tFr

omPh

ysic

alA

rtef

acts

toPr

oduc

tsT

heC

once

ptof

‘Pro

ject

’:A

Prop

osal

fora

Uni

fyin

gD

efini

tion

AR

TE

FAC

TS

INFO

RM

AL

ON

TOL

OG

Y

Art

efac

tcr

eate

dby

agen

tor

proc

ess

27

Task: Tasks are courses used to sequence processes or other perdurants that can be under the control of a planner.Organizations act by deputing tasks to roles performed by physical agents[10].

When a process is run, tasks go through one of these states7:

– Waiting means that the Task is not yet Ready.– In Process means that a actor has acquired the Task.– Done means that a actor has completed the Task.– Skipped means that the Task will never be Ready, but tasks that depend on the Skipped task can become

Ready.– Activated means that the Task has been automatically started without being associated with a actor. This state

applies only to Tasks with subtasks.

7 http://download.oracle.com/docs/cd/E13182_01/elink/bpo/bpo11/bpwrkflw.htm

28

Task

Sour

ceO

ntol

ogy

Sour

ceO

ntol

ogy

Sour

ceO

ntol

ogy

Prop

erty

Rea

son

prop

ertie

s,R

ange

full

nam

est

ring

(Ple

ase

Spec

ify)

Self

Driv

en

has

desc

ript

ion

stri

ng(P

leas

eSp

ecif

y)Se

lfD

riven

due

date

Dat

e(P

leas

eSp

ecif

y)B

usin

ess

Proc

ess

Des

ign

Ove

rvie

wD

ueda

te-d

ate

prio

rity

stri

ng(P

leas

eSp

ecif

y)B

usin

ess

Proc

ess

Des

ign

Ove

rvie

wPr

iori

ty-S

trin

g

stat

usst

ring

(wai

ting,

done

,...)

Bus

ines

sPr

oces

sD

esig

nO

verv

iew

Stat

us–d

iffer

ents

tatu

ses(

Rea

dy,w

aitin

g)ha

ssu

bta

skTa

skTa

skTa

xono

mie

sfo

rK

now

ledg

eC

onte

ntD

07Ta

skde

com

pose

din

toSU

BTA

SKS

ista

skof

Rol

eTa

skTa

xono

mie

sfo

rK

now

ledg

eC

onte

ntD

07A

ctor

hast

ask

task

sequ

ence

Proc

ess

Task

Taxo

nom

ies

for

Kno

wle

dge

Con

tent

D07

SKIi

ngw

ithD

OL

CE

:tow

ard

aSc

ienc

eK

now

ledg

ese

quen

ced-

by:T

ask

Task

sar

eco

urse

sth

atar

e(m

ostly

)use

dto

sequ

ence

activ

ities

spec

ializ

edby

Task

Task

Taxo

nom

ies

for

Kno

wle

dge

Con

tent

D07

task

spec

ializ

esta

skor

soci

alob

ject

spec

ializ

edby

soci

alob

ject

Task

sar

ea

kind

ofso

cial

obje

ct.

29

Process: Process is defined as transforming input elements in to some new output element. In organizations eachprocess has a responsible actor playing a role in it and organizes tasks into a structure that determines when eachtask is to be performed.

30

Proc

ess

Sour

ceO

ntol

ogy

Sour

ceO

ntol

ogy

Sour

ceO

ntol

ogy

Prop

erty

Rea

son

Prop

ertie

sR

ange

full

nam

est

ring

(Ple

ase

Spec

ify)

Self

Driv

enha

ssu

bpr

oces

sPr

oces

sA

nA

ppro

ach

toth

eD

efini

tion

ofa

Cor

eE

nter

pris

eO

ntol

ogy:

CE

O

SUPE

RPr

ojec

tD

eliv

erab

le1.

1ha

ssu

bpl

anSU

PER

proj

ectD

eliv

erab

le1.

1,th

atpr

opos

esU

pper

Proc

ess

Ont

olog

y(U

PO).

Acc

ordi

ngto

this

wor

kth

eco

ncep

tpla

nis

high

leve

labs

trac

tion

ofpr

oces

san

deq

uiva

lent

toa

proc

ess

inB

PMO

.Als

ose

eth

etr

ansf

orm

atio

nse

ctio

nby

CE

Ose

quen

ced

byTa

skTa

skTa

xono

mie

sfo

rK

now

ledg

eC

onte

ntD

07

SKIi

ngw

ithD

OL

CE

:tow

ard

aSc

ienc

eK

now

ledg

e

sequ

ence

d-by

-Ta

skTa

sks

are

cour

ses

that

are

(mos

tly)u

sed

tose

quen

ceac

tiviti

es

uses

Res

ourc

esT

heR

ole

ofFo

unda

tiona

lO

ntol

ogie

sin

Man

ufac

turi

ngD

omai

nA

pplic

atio

ns

AC

ore

Ont

olog

yfo

rBus

ines

sPr

oces

sA

naly

sis

Bus

ines

sA

ctiv

ityU

ses

Pers

iste

ntE

ntity

Proc

ess

isa

subc

lass

ofB

usin

ess

Act

ivity

and

Pers

iste

ntE

ntity

can

becl

assi

fied

asPh

ysic

alen

tity,

Non

phys

ical

Ent

ityan

dA

gent

s.In

Foun

datio

nalO

ntol

ogie

sin

Man

ufac

turi

ngD

omai

nth

eyha

vein

trod

uced

the

conc

epto

fres

ourc

esth

atco

rres

pond

sto

the

Age

nts

and

phyi

cale

ntiti

tes

buti

nou

ropi

nion

non

phys

ical

entit

eslik

eso

ftw

are

can

also

beco

nsid

ered

asre

sour

ce.

asso

ciat

edto

Eve

ntTo

war

dsan

Ont

olog

ical

Foun

datio

nfo

rSe

rvic

esSc

ienc

e

SUPE

RPr

ojec

tD

eliv

erab

le1.

1Se

rvic

epro

cess

impl

emen

tsa

serv

ice

serv

ices

are

tem

pora

lent

ities

(eve

nts)

and

Serv

ice

proc

ess

isa

Proc

ess.

Soea

chev

enth

asa

asso

ciat

edpr

oces

sth

atim

plem

ents

it.T

here

isan

othe

rwor

kby

SUPE

Rpr

ojec

tD

eliv

erab

le1.

1,th

atpr

opos

esU

pper

Proc

ess

Ont

olog

y(U

PO).

Acc

ordi

ngto

this

wor

kth

eco

ncep

tpla

nis

high

leve

lab

stra

ctio

nof

proc

ess

and

equi

vale

ntto

apr

oces

sin

BPM

O.

Thi

son

tolo

gyst

ates

the

prop

erty

ofas

soci

ated

even

t.pa

rtic

ipan

tA

ctor

DO

LC

ED

18E

ndur

ant

Part

icip

ate

inPe

rdur

ant

Act

oris

anE

ndur

anta

ndit

Proc

ess

that

isa

Perd

uran

t

prod

uce

outp

utA

rtef

act

From

Phys

ical

Art

efac

tsto

Prod

ucts

Art

efac

tcre

ated

byag

ento

rpro

cess

we

choo

seto

mod

ify

the

crea

ted

byto

prod

uce

outp

utbe

caus

eas

apr

oces

sha

sa

inpu

tand

itpr

oduc

eou

tput

.The

outp

utof

apr

oces

sca

nbe

apr

oduc

tora

Doc

umen

tand

thes

eco

ncep

tsar

eco

nsid

ered

asar

tefa

cts.

has

resp

onsi

ble

Act

orT

heen

terp

rise

onto

logy

Act

orre

spon

sibl

efor

AC

TIV

ITY

We

cons

ider

Act

ivity

asa

Proc

ess

31