collaborative ontology development for the geosciences

Collaborative Ontology Development for the Geosciences

Reza Kalbasi,* Krzysztof Janowicz,† Femke Reitsma,‡ Luc Boerboom* andAli Alesheikh§

*ITC, University of Twente†Department of Geography, University of California, Santa Barbara‡Department of Geography, University of Canterbury§KNT University of Technology

AbstractOntology-based information publishing, retrieval, reuse, and integration have become popular researchtopics to address the challenges involved in exchanging data between heterogeneous sources. However, inmost cases ontologies are still developed in a centralized top-down manner by a few knowledge engineers.Consequently, the role that developers play in conceptualizing a domain such as the geosciences is dispro-portional compared with the role of domain experts and especially potential end-users. These and otherdrawbacks have stimulated the creation of new methodologies focusing around collaboration. Based on areview of existing approaches, this article presents a two-step methodology and implementation to fostercollaborative ontology engineering in the geosciences. Our approach consists of the development of aminimalistic core ontology acting as a catalyst and the creation of a virtual collaborative developmentcycle. Both methodology and prototypical implementation have been tested in the context of theEU-funded ForeStClim project which addresses environmental protection with respect to forests andclimate change.

1 Introduction and Motivation

Throughout the history of Geographic Information Systems (GIS) the trend has been awayfrom mainframes and later single desktop GIS, to modular, web-accessible, and interchange-able services. With the advent of Volunteered Geographic Information (VGI), this trend awayfrom monolithic top-down solutions has moved beyond technology and software towardsdata. In the future, we will likely see the same movement from top-down schemata developedby authorities to users becoming active knowledge engineers (Janowicz 2010, 2012). This willbe especially important in the age of Big Geo-Data, which is characterized not only by largevolumes of data but also by the variety of these data.

These different approaches do not necessarily contradict each other but will most likelycoexist. This shift towards the active involvement of users as creators of data and knowledgefosters cooperation and supports exchange and sharing of geographic information. TheNational Science Foundation (NSF)’s envisioned EarthCube next generation knowledge infra-structure (http://www.nsf.gov/geo/earthcube/), for instance, is based on the idea that scientistsfrom multiple areas of the geosciences will make their data accessible in such a way that theycan be discovered, reused, and integrated by others (explicitly including researchers from otherdomains). This makes semantic interoperability and the collaborative development ofmetadata annotation vocabularies (e.g. ontologies) a crucial element (Berg-Cross et al. 2012).

To support this shift requires interoperability between services as well as compatibleconceptualizations underlying the collected and shared data. Challenges related to achieving

Address for correspondence: Reza Kalbasi, ITC, University of Twente, Enschede, The Netherlands. E-mail: [email protected]

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Research Article Transactions in GIS, 2013, ••(••): ••–••

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interoperability occur on three levels (Bishr 1998): syntactic, schematic, and semantic. ForGIScience, syntactic and schematic heterogeneity are handled by international standards devel-oped by the Open Geospatial Consortium (OGC) alongside ISO (TC211) through voluntaryconsensus. In contrast, semantic interoperability is still an ongoing research endeavor and aprerequisite for the Semantic Geospatial Web or service orchestration in general (Mark 1993;Bennett 2001; Fonseca et al. 2002; Brodaric and Gahegan 2007; Bröring et al. 2009; Lutzet al. 2009; Janowicz et al. 2010).

An example of this challenge of semantic interoperability is found within the ForeStClimproject (http://www.forestclim.eu/) where different communities have adopted various defini-tions for Forest based on application area, cultural background, political systems, and variousother factors. Lund, for instance, has collected over 950 different definitions for forest, defor-estation, afforestation and reforestation (Lund 2009). Compare, for example, the UN-FAO’sdefinition of forest: “. . . A land used mainly for wood production and other forest products,protection . . .” (http://www.fao.org/DOCREP/005/Y4357E/y4357e20.htm) with that of theEuropean Environment Agency (EEA): “. . . a vegetation community dominated by trees andother woody shrubs, growing close enough together that the tree tops touch or overlap, creat-ing various degrees of shade on the forest floor. It may produce benefits such as timber, recrea-tion, wildlife habitat, etc . . .” (http://www.epa.gov/trs). Although both definitions refer to theterm “Forest”, it becomes clear that the intended meanings differ; see also Bennett (2001) foran ontological perspective.

As formal specifications of conceptualizations, ontologies are a promising method forrestricting potential interpretations of a schema or set of concepts. Ontology alignment,matching (Euzenat and Shvaiko 2007), and similarity reasoning (Janowicz et al. 2008) canthen be used to establish relations between different ontologies and classes (Janowicz et al.2008) and, hence, to handle semantic heterogeneity. The engineering of ontologies, however, isa challenging task.

Ontology engineering methodologies can be divided into centralized ontology engineer-ing and collaborative ontology engineering. Three main roles are engaged in the process ofcreating and maintaining ontologies: the ontology engineer (OE), the domain expert (DE),and the end-user (Gruninger and Fox 1995; Janowicz et al. 2008). Ontology engineersare familiar with knowledge representation languages, tools and editors, reasoning services,and formal modeling paradigms. In contrast, domain experts are usually novices inontology engineering but provide the crucial domain knowledge and modeling experience,for example with respect to numerical models, their interpretation, and shortcomings.Documentation, databases, or encyclopedias alone cannot substitute for a domain expertas, unlike other information sources, an expert can provide active feedback and dis-tinguish between nuances that may escape the engineer’s attention. Finally, end-usersplay the role of consumers who apply the ontology to their application domain. Oneproblem is that these three roles do not speak a common language (Janowicz et al. 2008).The user may have a simplified, application-oriented, or naive perspective totally unlike thatof the domain expert. Neither can judge whether the design decisions taken by the knowl-edge engineer in implementing the ontology reflect their initial conceptualization (Janowiczet al. 2008).

In a centralized manner, the ontology engineer develops the ontology while not optimallyengaging the other roles. Based on a review of existing methodologies and their realization,centralized ontology engineering faces several shortcomings: (1) differences between the engi-neered ontology and the conceptualizations of experts and users can only be detected in thefinal product or milestones but not through direct interaction. This family of shortcomings can

2 R Kalbasi, K Janowicz, F Reitsma, L Boerboom and A Alesheikh

© 2013 John Wiley & Sons Ltd Transactions in GIS, 2013, ••(••)

further be divided into those based on geographic distance between the different parties, thosebased on missing user feedback and mis-conceptualizations in the application, and those thatcannot be detected based on reasoning over consistency, subsumption or similarity; (2) Oncedeveloped, the static state of ontologies collides with the dynamic nature of applications(Reitsma et al. 2009); and (3) Besides changes due to the dynamic nature of applications anddata sources, ontology evolution is another important aspect that describes the shift inconceptualization and belief. Examples are not limited to new scientific findings but are alsobased on drifts resulting from cultural or political changes.

Collaborative approaches to ontology engineering try to address some of these short-comings by focusing on the (real-time) interaction and cooperation among the three rolesinvolved in creating and maintaining ontologies. Through collaboration, the skills and theexperiences of different roles can be considered. For instance, the domain expert is not forcedto learn the principles of ontology creation. Rather they can control the modeled knowledgethrough an understandable infrastructure of ontology representation. In this work, andbased on past research on collaborative ontology engineering, geospatial-semantics, as well asgeo-collaboration, we introduce an approach to collaborative ontology development forknowledge reuse and sharing in the geosciences. The need for a situated and diverseconceptualization of geographic space makes geoscience an attractive domain of study (Mark1993; Bennett 2001; Brodaric and Gahegan 2007; Janowicz 2010).

In this article, we introduce a two-phased approach comprising the creation of a catalystontology, followed by the development of an ontology engineering methodology. In phase one,the catalyst ontology is developed as a minimalistic and initial ontology using a centralizedmethodology. We use the term catalyst to characterize this ontology as it incentivizes collabo-ration. The second phase is called Collaborative Ontology Development (COD) and intro-duces the framework and its implementation.

The remainder of this article is structured as follows. In Section 2, we highlight somerelated projects and research relevant for the understanding of our approach. Section 3 evalu-ates the current methodologies for collaborative ontology engineering. Based on this analysis,a new method is proposed in this section. Section 4 presents the application of the methodol-ogy to the EU ForeStClim project. Finally, Section 5 concludes the article and provides direc-tions for future work.

2 Related Work

In this section, we begin by surveying past research on geo-collaboration. This is followed by areview of major collaborative ontology engineering methodologies.

2.1 Geo-Collaboration

Geo-collaboration embraces GIScience, the physical sciences, human computer interaction(HCI), and psychology. Geo-collaboration is important because of the lack of group-support incommon GIS technologies, which are suitable for individuals. Its technologies should considerthe synchronous or asynchronous interaction of groups in co-located or distributed environ-ments (Hardisty 2009). Previous research has investigated directions such as geovisual repre-sentation, spatial annotations, discussions, trust, reliability, and accessibility. These directionshave been implemented in the context of SDI (geoportals), decision making (Carver et al.1998; Li et al. 2012), disaster management (Tomaszewski et al. 2007), and state-of-the-art

Collaborative Ontology Development for the Geosciences 3


Web 2.0 applications based on Volunteered Geographic Information. Prominent work in therealm of geo-collaboration includes the research of MacEachren and Brewer (2004) andMuntz et al. (2003), which considers the value of collaborative technologies for geospatialapplications. The focus of current geo-collaborative studies has been on making plans in prac-tice. In this context, geo-collaboration can assist in the implementation of plans (MacMillanet al. 2004), particularly in disaster situations (Kalbasi and Alesheikh 2011). From the geo-collaboration research, it can be conceived that visualization, especially on mobile devices (seeLooije et al. 2007), in both 3D and 2D, are important issues to resolve. While this research hasrevealed the importance of geo-collaboration, we are unaware of any work investigating thebenefits of collaboration in the realm of geosemantics. Our research aims at this gap for thedevelopment of geo-ontologies.

2.2 Collaborative Ontology Engineering

Co4 was one of the first methodologies to develop a consensual and formalized knowledgebase (Euzenat 1995). Since then, the major collaborative ontology development methodologieshave been proposed by Holsapple-Joshi (Holsapple and Joshi 2002), Karapiperis-Apostolou(Karapiperis and Apostolou 2006), and Tempich (DILIGENT) (Tempich et al. 2007). To inves-tigate and analyze these methodologies, some requirements for assessing their collaborativeontology development capabilities have been defined which form the basis for our comparison(explained in detail in Section 3.2).

In Holsapple-Joshi, Karapiperis-Apostolou, and DILIGENT, the role of the moderatorimpedes the ontology engineering process as they control and organize it. This may especiallyimpact large scale projects in which experts from multiple fields have to find a common agree-ment. In addition, the applied workflows in these methodologies bring about decision-makingagreements. These advantages have been taken into account in our research.

The methodologies developed by Holsapple-Joshi and Karapiperis-Apostolou are aimed atontology engineers while neglecting the roles of domain experts and end-users. Furthermore,the tools required to realize these methodologies (such as questionnaires and hard copy evalu-ation sheets) are not integrated into the current ontology editors. In addition, these methodol-ogies have supposed that the initial (catalyst in our terminology) ontology had been developedwhile abandoning the relationship between the catalyst and collaborative ontology and theyhave not considered the collaborative ontology development requirements in their methodol-ogy and workflows. The disadvantages of the DILIGENT methodology include that it has notelucidated different scenarios and events, which may affect ontology engineers, domainexperts, and end-users. In addition, some collaborative ontology engineering requirementssuch as ontology visualization, expressiveness, versioning, and periodic editing have not beentaken into account.

Besides these methodologies, two other significant approaches include the Human-Centered Ontology Engineering Methodology (HCOME) (Kotis et al. 2004), and ontologymaturing (Braun et al. 2007). The rationale behind HCOME is the use of personalconceptualizations (in personal spaces) by collaborators and comparing them with a sharedconceptualization. The problem with this methodology lies in the evaluation of personal andshared ontology dissimilarities where ontology mappings are cumbersome in practice(Stuckenschmidt and Van-Harmelen 2004). The ontology maturing methodology is based oncollaborative tagging inspired by Web 2.0 and is rather suitable for the development of light-weight ontologies.



3 Delving into Collaborative Ontology Engineering

In this section, a methodology for developing a catalyst ontology is presented, and the require-ments for collaborative ontology development are enumerated. Based on these requirementsand standards, we introduce our methodology.

3.1 The Methodology for Developing Catalyst Ontology

The essential factors for adopting a methodology for creating a catalyst ontology are themaintenance and evaluation tasks, as these are the joint points of the catalyst ontologydevelopment and collaborative ontology development. Gomez-Perez et al. (2004) evaluated anumber of methodologies and identified that many of them do not propose any detailsregarding these stages of ontology engineering. Examples include KACTUS (Gomez-Perezet al. 2004), Cyc (Lenat and Guha 1989), and SENSUS (Swartout et al. 1997). Of theremaining methodologies, including On-To-Knowledge (Sure and Studer 2002), Uschold andKing (Uschold and King 1995), and Gruninger and Fox (Gruninger and Fox 1995),METHONTOLOGY (Fernandez-Lopez et al. 1999) was selected. METHONTOLOGY isadopted in this research due to its completeness in covering all of the steps of ontology engi-neering, access to a best practice example of this methodology, and some deficiencies high-lighted in other methodologies mentioned by Fernandez-Lopez et al. (Fernandez-Lopez et al.1997).

3.2 Collaborative Ontology Engineering Requirements

Previous work has proposed a set of requirements to study collaborative ontology develop-ment methodologies and tools (Holsapple and Joshi 2002; Karapiperis and Apostolou 2006;Noy et al. 2006, 2007; Schaffert et al. 2006; Siorpaes and Hepp 2007; Tudorache et al.2008). The following list summarizes these prerequisites: (1) integration, representation, andstorage of discussions, annotations, and changes in ontology development; (2) ontologyexpressivity and ontology visualization, the ability to express and represent ontology in anunderstandable way for the collaborators; (3) continuous editing (starting the collaborationwithout any halt); (4) periodic archiving of different ontology versions; (5) determination of ajury as the panel of judges when disagreements arise (the administrator to manage andmonitor the discussions); (6) support for evaluation such as semantic/syntactic inconsistencychecking (checking the state of ontology during collaboration and change); (7) provenance ofinformation (history of changes and collaborators’ comments, activities, and profiles); (8)scalability (both in the size of ontology and the number of collaborators); (9) reliability (notto lose data); (10) robustness (of ontology editors); (11) the methods needed to reach a con-sensus when contradictory ideas exist (strategies to achieve agreement like voting); (12) accesscontrol (users’ restrictions to interact); (13) workflow support (to automate the process ofconsensus achievement and different roles and activities); (14) supporting synchronous col-laboration on ontologies (possibility for the collaborators to log in and act on an ontologysimultaneously); and (15) supporting asynchronous collaboration on ontologies. Among theserequirements, the determination of the jury and the consensus methods are only consideredfor the collaborative ontology development methodologies while reliability and robustness areexclusively platform requirements.



3.3 Proposing a New Methodology (COD) for Collaborative Ontology Development

Addressing the limitation in current approaches (see Section 2.2), a new methodology basedon Consensus Decision Making (Avery et al. 1981; Ellis and Fisher 1993) is presented in thisarticle, addressing the collaborative ontology development requirements. In Consensus Deci-sion Making, instead of simply voting for an item, and having the majority of the groupgetting their way, the group is committed to finding solutions where everyone has conveyedher/his thought to the others, and which all ultimately agree to. This ensures that everyone’sopinions, ideas and reservations are taken into account.

3.3.1 COD roles and tasks

As an organizer of a participatory ontology development process, the administrator managesthe policies, annotations, discussions of the collaborators, voting when debates arise, change,and storage of ontology. The administrator’s role is similar to a database administrator whomanages, maintains, initially design, and monitors the databases. As a member of an Informa-tion Technology (IT) team, an administrator should facilitate knowledge integration amongparticipants. While being familiar with formal knowledge engineering, domain knowledge inspatial science may only cover the basics of spatial database design and GIS. The administratorcannot make decisions personally, but only announces the decisions that are achieved by theparticipants. The second role is ontology engineer. Being knowledgeable in the fundamentalsof ontology development, its languages, development strategies, best practice, and differentlevels of formality, the ontology engineer handles the ontological components and axioms.Most importantly, ontology engineers need to be able to communicate the consequences ofparticular ontological commitments to the other roles, e.g. the domain experts. The use ofnatural language (NL) expressions, hierarchical, and tree-based, visualizations of ontologiesare the most common methods for ontology representation. In our COD framework, theontology engineers have permission to annotate but not to change the ontology. The adminis-trator can only change it after achieving a decision by the collaborators. This restriction canimpede any inconsistency and divergence in the process of ontology development.

In our methodology, Domain Experts should be more involved in the other tasks of devel-opment such as maintenance and evaluation (not only the knowledge acquisition task). CODhelps them understand the rationale behind the ontology by translating it into NL sentencesand through the use of tree-based (superclass/subclass relations) visualization of the ontology.As with ontology engineers, they are capable of annotating, but not changing the ontologyelements.

The last role is the end-user who is typically not engaged in the immediate process of ontol-ogy development. End-users rather access an ontology-based application, e.g. a tool for thesemantic annotation of scientific geo-data. It is often desirable that the ontology and the under-lying reasoning is transparent for end-users, i.e. that they do not have to interact with them.Domain experts and end-users overlap in the sense that most users of a system like EarthCubeare domain experts themselves without the need for all of them to invest time in working onontologies. In COD, end-users can send feedback regarding the applicability of the ontologies totheir tasks and data or report bugs within the ontology-based application to the administrator.

3.3.2 COD workflow for collaboration

Based on the introduced COD roles and tasks, we propose a new COD workflow (Figure 1).An activity can be started in four different ways. Firstly, an end-user’s feedback is transmitted



Figure 1 The proposed workflow for COD; in a client-server architecture the discussion threads (inblue) are performed. The final process of decision making is illustrated inside the red box



to the collaborators via the administrator. In this case, the administrator will annotate anontology component if the feedback is related to the ontology. Secondly, corresponding to theresults of the evaluation task, the administrator annotates related ontology components tostart the collaboration. The administrator announces results of discussions explicitly for theothers. In the third step, the domain expert commences the collaboration. They review andannotate the corresponding hierarchy and NL sentences of ontology elements or continue anydiscussion thread started by the others if necessary. Other domain experts will be notifiedabout the latest discussion threads and annotations by reviewing the history of these events. Inthe fourth step, the ontology engineers will be involved in a similar manner to the domainexperts. However, the engineers will not work on the more abstract level of diagrams andnatural language. Instead, they will consider particular aspects of the axiomatization and theconsequences of constructs expressed in the used ontology modeling languages.

Throughout the activities (as depicted in Figure 1), two other states will be anticipated:the discussions among the collaborators will result in consensual decisions or innonconsensual decisions (see Figure 1; the lower part of flowchart). In the case of disagree-ment, a voting process is performed by the collaborators and the administrator announcesthe final result. In this case, an exception has been considered for the voting process (illus-trated in red). The opponent, a collaborator who does not agree with the ultimate decision,can veto the decision if he/she can convince one or more participants to change their mind.These two alternatives are part of the consensus decision making process (explained inSection 3.3).

3.3.3 COD annotation types

In order to further enhance collaborative ontology engineering, we extend the range of previ-ously proposed annotation types (Kahan and Koivunen 2001; Tudorache et al. 2008) with ageospatial dimension. As a simple example, we use a GoogleMap annotation type here, otheralternatives are possible. This map annotation type aims at the representation of geospatialfeatures using geo-tagged images, e.g. from Panoramio, one of the initial facilities for the userof Google Maps and Google Earth to share photos of geographic features via tagging (http://www.panoramio.com). The purpose is to share the corresponding images of geographic fea-tures in case their meanings are controversially discussed among experts. This technique hasplayed an eminent role in explicating the meaning of similar geographic features for VGIs likeOpenStreetMap (Mooney and Corcoran 2012). For example in OpenStreetMap (http://www.openstreetmap.org/), Orchard and Forest concepts are distinguished by representing twoimages of these geographic features (http://wiki.openstreetmap.org/wiki/Vegetation). However,due to the miscellaneous cultural backgrounds or personal tendencies of collaborators, differ-ent images, diagrams, maps, and analysis results can be shared for one concept during the dis-cussion threads.

4 Use-Case: The Illustration of COD Methodology

In this section, we implement and evaluate the usefulness of our proposed methodology bycomparing its results with those of the centralized approaches. This evaluation was carried outin the context of the European ForeStClim project. In this project, two geospatial organiza-tions, ONF (http://www.onf.fr) and SERTIT (http://sertit.u-strasbg.fr) exchange GIS datasets,



where ONF provides the datasets of different tree species for SERTIT in order to process theknowledge-based classification of satellite images. The aim of this classification was to extracttwo more general classes of trees: coniferous and deciduous.

As semantically enriched information sharing (among heterogeneous sources) necessitatesontology mapping challenges, our implementation infrastructure is based on a “hybrid ontol-ogy engineering approach” to refrain from the ontology mapping obstacles (Wache et al. 1999;Stuckenschmidt and Van-Harmelen 2004). In a hybrid approach, the source ontologies of thetwo organizations (ONF and SERTIT) are built upon a shared ontology. The platform used tobuild the ontologies for this research was Protégé, a free, open-source ontology editor andknowledge-base framework (http://protege.stanford.edu/), using its collaborative ontologyengineering plugin.

4.1 Current Methodologies: Centralized Ontology-Based Geoinformation Sharing

In the first scenario, we exchanged geo-information where corresponding ontologies of eachorganization’s classification and the relevant shared ontology were developed in a centralized(traditional) manner.

In the hybrid approach to develop the shared ontology, bridge concepts were extractedfrom the UpperCyc ontology (http://www.cyc.com) as a consensus knowledge-base whichincludes standardized concepts in the forestry domain; these were the main classes of ourshared ontology (Wache et al. 1999; Stuckenschmidt and Van-Harmelen 2004). Bridge con-cepts should be concrete, general, and higher level and thus suitable for partial ontologymapping among miscellaneous sources.

In Figure 2, the hierarchy of the developed shared catalyst (centralized) ontology is illus-trated. By using the Pellet reasoner (Parsia and Sirin 2004) to evaluate the correctness of theontology, a semantic inconsistency was found, namely that one of the classes “LarchTree_cyc”(the class for larch trees) was the subclass of two disjoint classes “Deciduous_plant_cyc” (the

Figure 2 The developed catalyst shared ontology before modification; after modification,LarchTree_cyc became the subclass of Coniferous_tree_cyc class (the relationship in Blue color wasremoved)



class for deciduous trees) and “Coniferous_tree_cyc” (the class for coniferous trees). Duringthe knowledge acquisition process, it was determined that each tree species cannot be bothconiferous and deciduous at the same time. After referring to the ONF metadata (data pro-vider), the larch tree was assigned to be a subclass of the “Coniferous_tree_cyc” class.

Based on the developed catalyst shared ontology, the catalyst ontologies of two organiza-tions were developed using the Web Ontology Language OWL DL (http://www.w3.org/TR/owl-guide/). At the end, SERTIT used the GIS layers as the auxiliary knowledge in theirclassification processing.

4.2 Proposed Methodology: Collaborative Ontology-Based Geoinformation Sharingby COD

In a second scenario, the proposed COD methodology was applied to the ForeStClim project(the outputs of Section 4.1). Based on the COD workflow (see Figure 1), the catalyst ontol-ogies were stored in the server. In our implementation, one administrator, one ontology engi-neer, and three domain experts participated. Below, we present two of the many discussionthreads in order to illustrate how the workflow materializes in a real setting.

I) Thread one

In one case, the ontology engineer found a semantic inconsistency via the Pellet reasonerduring the evaluation of the ontology (see Figure 3). The reason for this inconsistency was thatthis class, “Other_ONF” class, was the superclass of some disjoint classes. The ontology engi-neer started a discussion thread by annotating this class (see Figure 4, Part A). Afterwards, theother roles were notified through the review of changes and annotation board (All note tab inFigure 4). The discussion thread determined that such a modeling mistake was a result of notspecifying which area is represented in the geographic extent by this ontology class (during theknowledge acquisition task).

In this discussion thread, a collaborator annotated this concept using the maps annotationtype (see Figure 4, Part B). This collaborator had personal experience (through field-work) offorest management in this specific area and so, cognizant of the exact semantics of this class,

Figure 3 Utilizing the reasoning services, Pellet reasoner 1.5.2, to detect semantic inconsistency inOther_ONF class



could resolve this problem voluntarily. He used the GoogleMap annotation type and uploadedthe geo-tagged images of this area on Google Maps (see Figure 5). In a similar fashion to therecent movements towards user-generated and volunteered information systems (like VGI),this proves that collaborators are valuable sources of up-to-date knowledge for dynamicdomains such as the geosciences (Goodchild 2007; Kuhn 2007).

Figure 4 The GUI of discussion threads in COD; in this running, collaborators are discussing the“Other_ONF” class, since its semantic inconsistency was spotted using the Pellet reasoner



II) Thread two

As in the centralized scenario (Section 4.1), the ontology engineer spotted and annotated asemantic inconsistency in the “LarchTree_cyc” class (as a subclass of two disjointsuperclasses). Another discussion thread commenced with five collaborators. Due to the con-sensus decision making, one of the domain experts could change the ideas of the other partici-pants by explaining and proving his opinion. It was determined that the larch tree can be bothconiferous and deciduous from one point of view since it has needle-shape leaves but sheds itsleaves during winter (stored knowledge in Cyc ontology). Nevertheless, in our project, theyshould be disjoint as two different classes of a thematic map (classification of satellite images).As a result “Persistent_shared_collab” and “Deciduous_shared_collab” classes were intro-duced by the collaborators instead of “Coniferous_tree_cyc” and “Deciduous_plant_cyc”classes (in the catalyst shared ontology), respectively. Due to the SERTIT classification andmetadata, “leaf shedding” is the main factor for the distinction between these classes. Asthe larch tree loses its leaves in winter, it was not considered as a subclass of the“Persistent_shared_collab” class. Thus, it became the subclass of “Deciduous_shared_collab”class while being a subclass of “Coniferous_tree_cyc” class in the centralized scenario. Thiswas an important example for the use of COD, since the participation and understanding of

Figure 5 Uploading the geo-tagged image of an ontology concept; this clearly represents an ontol-ogy individual as a geographical feature in real world



the knowledge modeling process (ontology development) by the collaborators resulted in a dif-ferent shared ontology from the centralized scenario. The other prominent task in this phasewas the representation of ontological axioms by NL expressions. This technique helpedthe collaborators understand the ontology components clearly. For instance, some of theontological axioms related to “Deciduous_SERTIT” class are: ∃lose_leaves_in seasons AND∃lose_leaves_in winter AND ¬non_tree_land as the sufficient and necessary conditions as wellas Deciduous_SERTIT SERTIT_classification as the necessary condition. In addition tothese formal conditions, a NL statement was added to this class of ontology: “It is a memberof SERTIT classification, and it is NOT a non-tree land, and it always loses its leaves only inwinter”. The reason for using ∃lose_leaves_in seasons condition is to guarantee the existenceof lose_leaves_in relationship. This is something that the current translators cannot translateinto NL as they are not intelligent enough.

Based on the built collaborative shared ontology (see Figure 6), the other ontologies (ofthe SERTIT and ONF organizations) were developed collaboratively. In summary, eight otherdiscussion threads embracing 49 conversations were made with these ontologies (see Table 1).

One type of error spotted by COD was “incompleteness of concept names”. This type oferror in ontology evaluation is not recognizable by using any reasoner. At the end, as thesecond run, the requester organization (SERTIT) used the GIS layers of provider in their clas-sification processing, which resulted in a better classification and higher total accuracy thanthe centralized scenario in Section 4.1.

5 Conclusions and Recommendations

Semantic heterogeneity can be addressed by the development of ontologies as the formal speci-fication of conceptualizations. In this article, we have shown that collaborative ontology devel-opment can overcome the deficiencies of traditional methodologies, which neglect some

Figure 6 The collaborative shared ontology; OWLViz is applied to illustrate the hierarchy of thisontology



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essentials scenarios such as the dynamic state of ontologies, the interaction of ontology engi-neering roles, and the need for ontology maintenance. In this article we present a state-of-the-art methodology, COD, which addresses the drawbacks of recent proposed collaborativeontology development methodologies to fulfill the corresponding requirements (see Section3.2) for this type of ontology building. It is worth noting that a similar approach was success-fully used for the collaborative development of the W3C Semantic Sensor Network ontology(Compton et al. 2012). While the implementation following each round of discussions wasmostly centralized, the ontology was built on top of a small core ontology, the Stimulus-Sensor-Observation ontology design pattern, which was developed as a minimal set ofcommon agreements among the group members. The current trend to so-called Geo-Vocabulary Camps (GeoVoCamps) can be interpreted along similar lines. Workshops in whichdomain experts and ontology engineers work together to produce highly modular, self-contained, and reusable building block ontologies that can act as starting points for the devel-opment of more complex ontologies.

Based on the proposed methodology and the outputs of our implementation, we haveachieved the following goals:

• Supporting discussion threads, changes, and annotations by COD facilitated the collabora-tors to tag, notify, and discover the critical components of an ontology. In our implementa-tion, through eight different discussion threads and 49 conversations (among thecollaborators) semantic inconsistencies were identified and resolved.

• While the use of natural language and a hierarchical representation of the ontology facili-tated the collaboration of domain experts (unfamiliar with ontological modeling), thetransformation from an ontology language to natural language sentences remains a majorchallenge.

• Since the COD workflow is based on consensus decision-making (see Figure 1), the deter-mination of different roles steered our implementation without any contradiction about theresponsibility of collaborators. Nevertheless, we could not evaluate a large number of par-ticipants and probable arising difficulties. In addition, the delivery of end-user feedback tothe administrator was not tested. However, research on end-user feedbacks is investigatedin parallel works such as Maué (2009).

• Checking the consistency of the ontology was executed automatically by using the Pellet rea-soner. It helped us to spot and modify the semantic inconsistency of some classes through thecollaboration. We have determined that this requirement is the heart of collaborative ontol-ogy development methodologies as they aim at ontology refinement and maintenance.

• The main sources of errors identified by COD were semantic inconsistency and incomplete-ness of concept names.

Finally, the results of this research demonstrated that collaboratively built ontologies werebetter (with fewer ontological errors) than the centralized one. The proposed method can beused to reveal and resolve inconsistencies which cannot be addressed automatically, i.e. by rea-soners such as Pellet. In addition, extending the current annotation types with geospatialdimensions was useful to explicate the semantics of similar geographic features. As a case-study, the exchanged geo-information through our infrastructure increased the total accuracyof the knowledge-based classification of satellite images. In our future work, we will investi-gate the following:

• A semantic component for GIS software is needed as the separation of ontology editors likeProtégé (in this research) with GIS platforms results in difficulties for interoperability. The



semantic component of GIS or Remote Sensing software can be derived from a number ofdifferent levels, such as the metadata, the field headers or the values of the attributes at thefeature level (Batcheller and Reitsma 2010; Reitsma and Tanasescu 2010).

• We are going to delve into advanced ontology visualization techniques to support easiercollaboration. Two of the solutions for proper visualization are the use of NL expressionsand the tree view. Some research efforts such as AceWiki (http://attempto.ifi.uzh.ch/acewiki/) have focused on the translation of ontologies into NL (Kuhn 2008). Althoughthey are not dextrous enough at the current time, future research can make use of them.

• While we evaluated the developed ontologies via reasoning services, other parameters ofontology evaluation such as technological, structural and conceptual parameters shouldalso be addressed.

• Finally, due to the policies and limitations of our project, we were unable to address theend-user role feedback. This is one of our future aims, where we aim to extend our meth-odology capabilities through the amalgamation of VGI and COD.

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