Argumentation-Based Ontology Engineering

Argumentation Technology

Christoph Tempich and Rudi Studer, University of Karlsruhe

Elena Simperl and Markus Luczak, Free University of Berlin

H. Sofia Pinto, Technical University of Lisbon

he Semantic Web envisions an infrastructure in which humans and machines seam- T lessly exchange information on the Web. For this to succeed, we need shared ontologies that enable information exchange between different parties. Engineering a shared ontology in this kind of scenario is a social, evolving process, involving a geographically The DILIGENT dispersed community with different knowledge and The DILIGENT (distributed, loosely controlled, and argumentation expertise—ontology engineers, domain experts, and evolving engineering of ontologies) argumentation ontology users. framework consists of an argumentation process and framework helps Depending on the methodology used, the ontol- a formal model, and is supported technologically by ogy’s life cycle might consist of a series of stages, in a wiki-based support tool, coefficientMakna (col- capture design which the engineering team decides how to model a laborative, ontology-engineering-efficient Makna). domain to best suit the ontology users’needs and the Here, we sketch out DILIGENT’s theoretical back- deliberations in application requirements.1 Such engineering method- ground and an application scenario (typical to dis- ologies typically cover the major aspects of general tributed ontology engineering) we envisioned when ontology-engineering ontology engineering, describing related support specifying the framework’s functional requirements. methods, decisions to consider, and expected results. We also describe coefficientMakna’s design, archi- discussions. It makes However, these methodologies only marginally tecture, and implementation. We employed several address the collaborative interaction among commu- case studies to provide empirical data on the frame- consensus-building nity members who are creating an ontology. For work. We paid particular attention to lessons learned example, consider this two-phase methodology: first, from a study in which a team of eight individuals tasks more efficient a selected team of ontology developers creates an ini- with minimal experience in ontology engineering tial ontology; second, the development team contin- successfully developed a cooking ontology using and provides detailed uously extends and refines the ontology on the basis coefficientMakna. of feedback from a panel of domain experts.2 The guidance for interaction between the participants occurs indirectly, Application scenario and use cases with the core development team acting as a mediator We aim to use the DILIGENT argumentation frame- nonexperts. between the different parties suggesting changes to work in collaborative ontology engineering. The tool the ontological content. Most ontology-engineering primarily supports conceptualizing and formalizing environments also employ this indirect style. shared ontologies, complementing native ontology In contrast, our argumentation framework pro- editors that focus on implementation tasks. (We point vides detailed ontology-engineering guidance explic- readers elsewhere to learn more about the distinc- itly supporting direct interaction between ontology tion between conceptualization, formalization, and engineers and domain experts. This occurs through implementation in ontology engineering.1) arguments, which fosters consensus building and the In a distributed-ontology-engineering scenario, a creation of a truly shared ontology. geographically dispersed community incrementally

Authorized licensed use limited to: UNIVERSITY OF ALBERTA. Downloaded on October 1, 2009 at 14:32 from IEEE Xplore. Restrictions apply. develops an ontology—or multiple versions of it—reflecting the community members’ shared view with respect to the modeled domain (see figure 1). The participants, including domain experts and observers, don’t necessarily have an ontology-engineering background. Building on changes in the target domain, evolving application require- Argue Argue ments, and discussions on whether and how Issues to model specific domain aspects, the engi- Ontology engineers #1 Optional: acquired users from neering team continuously revises and ex- s P nt o e cision s the target group De s it tends the ontology and releases new ver- m io u n

Argue g s sions. To effectively participate, new parties r Persistent joining the community must understand the A ontology rationale behind modeling decisions and fol- versions low the ontology’s release history. E Argue Ontology engineering usually starts by ana- laborations lyzing the domain and application require- Board Argue Ideas ments. The developers agree on these require- Argues ments and their importance, and propose and discuss different ways to build a model Optional: that complies with them. They must discuss ontology engineers #2 Customer whether to model specific domain information as classes, instances, attributes, or properties, Figure 1. A collaborative ontology-engineering scenario. to decide on an adequate granularity level for the model and on conventions for labeling and documentation. Finally, they implement the stated that nutrition values were not available for argumentation support and formalization, conceptualization in a formal knowledge rep- for the ingredients, without providing an alter- which are unique to ontology engineering: resentation language such as the Semantic Web native to resolve the issue. ontology language OWL DL (Web Ontology The participants informally state a common • You can apply general argumentation Language Description Logic). They can revise goal for the ontology and introduce ideas for models across various disciplines, which the ontology according to user needs once new its resolution on a conceptual level. While cover many argument types. However, our domain knowledge is available. Later, differ- all agree on the common goal, a participant studies highly recommend (and even re- ent parties might maintain and use different disagrees with the proposed conceptualiza- quire) limiting the set of arguments to versions of the same ontology, while a shared tion. We developed methods that support improve such general models’usability for version has changes integrated into it. To facil- capturing such deliberations and help detect ontology engineering.6 itate the shared ontology’s systematic evolu- conflicts. • You can’t easily detect inconsistencies in tion and to operationalize consensus building, informal argumentation models’ discus- the developing team should discuss such issues Requirements sions because arguers don’t formalize in a controlled manner and trace the engineer- From an argumentation point of view, we their arguments. This might be a secon- ing process and results achieved to date. They can categorize ontology-engineering discus- dary consideration for general topic dis- should ensure a seamless development of the sions as deliberation dialogues in which the cussions such as requirements analysis, shared ontology, using instruments to resolve participants “collaborate to decide what ac- but it becomes crucial for knowledge for- conflicts that arise when several parties hold tion or course of action should be adopted in malization tasks. Ontologies are formal irreconcilable views. some situation.”3 The IBIS (Issues-Based models that must be semantically consis- We exemplify the scenario and its require- Information System) methodology provides tent to be of value to the applications ments using a discussion from a case study, an argumentation theory for deliberation dia- using them. Inconsistencies in discussions in which the participants built a cooking on- logues. Developers have used IBIS in soft- on how to ontologically represent domain tology. A short passage from the study illus- ware and requirements engineering to cap- knowledge lead to inconsistent models of trates the main issues in collaborative ontol- ture design deliberations.4,5 Researchers have lower usability. ogy engineering: developed formal models to allow for struc- • You can build ontologies in various ways, One of the participants stated the requirement tured queries on the collected arguments. with manual building and automatic learn- that the developed ontology should include Such approaches show that formal argu- ing as prominent examples. The latter helps guidelines for a low-fat diet. All members mentation models ease design-decision trace- produce large amounts of data but is less agreed with this requirement. As an idea to ability, help in conflict resolution, enhance suited for structuring this data into man- resolve this requirement, one participant stated reusability, and facilitate integrating new par- ageable pieces. Argumentation models can that this would require the inclusion of nutrition values into the ontology. At this point ticipants. Although these are general mod- help provide structure, offering an inte- another participant challenged this idea and els, they let us identify several requirements grated view on manually and automatically

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created ontologies (or interrelated ontol- empirical findings, so that the ontology-engi- However, they should permit exporting the ogy fragments). neering discussions are focused and efficient. model according to different formalisms.

We considered these requirements along Focus on ontology design processes. Build- Include inferencing. The tool should include with those derived from the application sce- ing on other studies’ results,7 we should inferencing capabilities to ensure the mod- nario when developing the functional re- extend general-purpose argumentation modeled knowledge’s correctness. quirements for the DILIGENT argumentation els such as IBIS with domain-specific knowl- framework. DILIGENT’s requirements corre- edge to apply them in specific use cases. The Support collaboration. It should also sup- spond to its three main aspects: process, developed argumentation ontology should port distributed access to the ontology and argumentation ontology, and tool support. thus reflect the particularities of ontology discussions on issues related to ontology design processes. Detecting contradictory development. Argumentation process information at the discussion level is impor- The requirements for this aspect fall into tant because such contradictions lead to onto- Support externalization. The tool should three categories: logical inconsistencies. support externalization of the rationales underlying the modeling decisions to let new Support argumentation process. The frame- Focus ontology domain. Minimal ontologi- participants trace past discussions and results work should support the full argumentation cal commitment ensures an ontology’s exten- achieved. cycle. This includes raising issues, mediat- ing conflicts, bargaining, clarification, and Provide ontology access. During the discus- agreement. Participants also aggregate lines sion, the ontology might evolve along dif- of reasoning to systematize their argumenta- The system should ferent tracks. Although participants have tion, so they must know which issues are agreed on some parts of the ontology, others under discussion, postponed, agreed, and dis- detect contradicting might still be under discussion. To let par- carded at any time. ticipants evaluate and compare the different lines of reasoning within revision proposals, the tool should be able to Support conceptualization and formaliza- visualize concurrent versions of the ontol- tion. People might agree on the need for a argumentations and ogy, identify commonly agreed fragments, certain conceptual model but not on how to and export the conceptualization to a formal implement it in a particular knowledge rep- discussions to speed up knowledge representation language. resentation language or formalism. The framework should support argumentation for Detect contradictions and conflicts. The sys- both conceptualization and formalization. the engineering process. tem should detect contradicting lines of reasoning within argumentations and discus- Provide methodology. The framework should sions to speed up the engineering process. provide process support (for example, a sibility for future uses.8 An ontology should Later, the participants will need methods process-driven methodology) for systematic model only one domain at a time, and ontol- and tools to mediate conflicts and reach ontology development. It should guide users ogy engineers should avoid integrating many agreements. through the necessary steps and activities, different issues in a single ontology module. the expected intermediary results, and the So, the argumentation ontology should focus Provide discussion status. Requirements and optimal way to use the results to accomplish on the argumentation-related ontological modeling decisions should have a status level particular goals. entities. to help ontology developers easily assess the overall progress. The tool should group Argumentation ontology Avoid encoding bias. The argumentation issues and ideas according to their priority The requirements for this aspect fall into ontology should be independent of the for- and should visualize which issues are agreed five categories: malism used to model the final ontology. on, under discussion, or postponed. Each formalism allows for different sets of Use established terminology. Computer sci- modeling decisions, and all can be subject to DILIGENT argumentation ence has a long history of researching argu- discussion. framework mentation and argumentation visualization.5 The DILIGENT argumentation framework The acceptance of DILIGENT’s argumentation Tool support consists of two building blocks: an argu- ontology and its impact beyond the bound- The requirements for this aspect fall into mentation process description and an argu- aries of the Semantic Web community de- seven categories: mentation ontology, which are supported pend on the use of established terminology technologically by coefficientMakna. for this research field. Abstract from implementation language. Ontology-engineering environments should Argumentation process Focus on relevant arguments. You should as far as possible abstract from a concrete description restrict the types of arguments formalized in implementation language such as OWL or We divide the argumentation process into the argumentation ontology according to RDF Schema and focus on modeling aspects. five activities:

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Authorized licensed use limited to: UNIVERSITY OF ALBERTA. Downloaded on October 1, 2009 at 14:32 from IEEE Xplore. Restrictions apply. Choose moderator. The participants in an in this context.) We further evaluated the dia- Argumentation ontology ontology-engineering discussion choose a logues, counting the number of agreed-on The argumentation ontology formalizes the moderator. The basic rules for moderation issues and analyzing the discussions’clarity arguments exchanged during ontology-engi- apply: the moderator doesn’t contribute to and the participants’satisfaction. Our analy- neering discussions. In contrast to the work the discussion or decision but structures the sis showed that restricting arguments in- on argumentation theory in agent commu- discussion and organizes the decision pro- creases all three aspects (agreement, clarity, nication, DILIGENT’s argumentation ontol- cess. Any participant can be the moderator, and satisfaction). ogy doesn’t aim to capture exchanged argu- and the moderator role can move from one From RST terminology, we selected the ments in a highly axiomatized manner. It participant to the next. All participants should arguments that proved to contribute to the doesn’t support automatic negotiation be- accept the moderator. discussions’ systematization in our experi- tween agents,10 but it does define the argument ments and that ontology developers acknowl- types that humans tend to use in ontology- Choose decision procedure. The participants edged as valuable: engineering discussions. agree on a mechanism for reaching agree- Our argumentation ontology’s main con- ment during the discussions. They decide on • Elaboration. An elaboration presents addi- cepts are issues, ideas, and arguments (see fig- a voting procedure, such as majority voting, tional details about the matter of discourse. ure 2). The argumentation ontology extends and on the conditions triggering a new voting Possible elaborations include introducing the IBIS methodology’s argumentation the- round—such as voting within fixed time in- members of a set, instantiations of an ory and has the semantics implied by OWL tervals or if no one has brought forward a DL. The argumentation ontology adapts the new argument within a certain time. original IBIS model to the ontology-engineering discussions’requirements. In this con- Specify issues. Issues correspond to the During the discussions, text, an issue introduces a new requirement or ontology’s domain or application require- topic in the discussion from a conceptual point ments. The participants must agree on a start- participants raise new issues of view. The issue helps explicitly separate the ing set of relevant issues. During the discus- discussions related to a domain’s conceptual- sions, each participant can raise new issues or or elaborate on existing ones. ization from the actual formalization or imple- elaborate on existing ones. Once a discussion mentation. Elaborations are extensions of evolves, participants can group issues and act Once a discussion evolves, existing issues. Ideas respond to issues and on them according to their priority. refer to their formalization; users discuss in participants can group issues terms of ideas how to formally represent Provide arguments and ideas. The partici- domain knowledge in the ontology—for pants discuss issues, propose ideas, and bring instance, as a class, an instance, a relation, or forward arguments in favor of or against an and act on them accordingly. an axiom. Accepted ideas trigger concrete issue or idea. The participants first agree on ontology change operations. an issue’s relevance and then suggest a way To foster consensus building, participants to formalize it. An idea describes in natural abstraction, parts of a whole, stages of a express positions and exchange arguments language how to formalize an issue. The par- process, object attributes, or specializa- on ideas, issues, and ontology entities. Justi- ticipants express their agreement or dis- tions of a general issue. fications are arguments for (pro) an idea or agreement with arguments and provide alter- • Evaluation and justification. An evalua- issue. Challenges are arguments against native ideas to strengthen or weaken them. tion provides a measurable advantage for (con) an idea or issue. The ontology further The entire process’s effectiveness and effi- a particular matter of discourse in com- differentiates arguments in favor through ciency depends on the decisions made on the parison to another. A justification gives examples and evaluations. CounterExamples basis of the provided arguments. So, we’ve evidence that something or someone has and alternatives are two classes of challenges analyzed this phase in detail and found that the authority to make a statement. that are particularly useful in ontology-engi- unguided discussions tend to have poor out- • Alternative. An alternative is a compara- neering discussions. comes. General-purpose argumentation frame- ble solution for the matter of discourse. Within a discussion thread, participants can works such as IBIS don’t restrict the types of • Example. An example (in RST, also called state positions. They clarify their position-on arguments that participants can provide. In evidence) of a particular matter of dis- an issue, idea, or argument by exclusively unrestricted ontology-engineering discus- course increases the belief in the corre- declaring their agreement or disagreement. sions, participants tend to make many con- sponding issue or idea. Once enough arguments and positions are cessions without making much progress • CounterExample. A counterexample pro- available, the engineering team can take deci- in the modeling. So, we analyzed the ex- vides counterevidence for a particular mat- sions (associated with issues, ideas, and ontol- changed arguments with the help of Rhetor- ter of discourse and decreases belief in an ogy entities). A decision’s status can be under- ical Structure Theory.9 (RST offers an expla- issue or idea. discussion, postponed, discarded, and agreed. nation for text coherence. It assumes that A decision records the issue on which it was each part of a coherent text has a particular Decide on issues and ideas. The participants taken, the positions constituting the final vote function, and so, a particular argument type. agree or disagree on an issue or postpone its on the issue (with-votes [several positions]), Researchers have already identified and resolution. Agreed-on ideas become part of and the line of reasoning (a sequence of argu- loosely defined 30 different argument types the shared ontology. ments) underlying the decision. A decision

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elaboratesOnIssue

Decision Issue hasStatus: onIssue raisedIssue: givenBy rdfs:Literal rdfs:Literal

withVotes Elaboration Actor responsesToIssue givenBy name: Idea rdfs:Literal OntologyEntity formalizesIdea label: rdfs:Literal givenBy ontoChange: rdfs:Literal summarizesArgumentation argumentsOn 1..* Argument Argumentation hasArgumentation 1..* positionOn positionOn positionOn Position Challenge Justification Human-Argumentation Machine-Argumentation providesText: rdfs:Literal tf/idf: rdfs:Literal

Disagreement Agreement Alternative CounterExample Evaluation Example

Figure 2. The core of the DILIGENT argumentation ontology.

can also be on-idea (the idea formalizing an The coefficientMakna system Makna.ag-nbi.de) in terms of design, archi- issue). This allows one to focus on the rele- Wikis have received increasing attention tecture, and usage principles. Like Makna, vant arguments. in the Semantic Web community.11 This we implemented it in Java based on the wiki Arguments are given-by actors,which are popularity is probably due to the core tech- engine JSPWiki (www.jspWiki.org), whose either humans or machines. Different kinds nology’s ease of use—a feature not neces- functionality we extended according to the of actors provide different argumentations. sarily characteristic of semantic applica- application scenario’s requirements. Humans (HumanArgumentation) tend to tions—and to their focus on collaborative argue by providing strings of text stating (pro- and community aspects. Existing semantic Ontology creation vides text) their reasons, and machines tend wikis primarily support the creation of and management to use argumentation measures such as Fre- semantic (instance) data expressed in Se- Users can create ontologies from scratch quency and TF-IDF (term frequency-inverse mantic Web languages such as RDF Schema or import existing ontologies to the wiki. document frequency). For each algorithm or OWL. (For a recent overview of existing When importing an existing ontology, the used, developers must introduce new sub- semantic wiki engines, see http://wiki. system maps the ontology model to the wiki classes of argumentation to model the differ- ontoworld.org/index.php/Semantic_Wiki_ hypertext model according to a predefined ent kinds of measures. State_Of_The_Art.) As with native ontol- schema. The system describes each ontolog- A reasoning mechanism can alert users if ogy editors, these wikis address ontology ical primitive using a dedicated wiki page. they agree and disagree on the introduction of development at the implementation level at The wiki page corresponding to a class in- the same ontology entity. This can happen best, assuming that the wiki’s collaborative cludes information about the direct sub- unintentionally if concepts are intercon- nature inherently eases the engineering classes and superclasses and instances, and nected through a complicated inheritance team’s consensus building. lists the labels and associated comments doc- hierarchy or in the case of long-lasting dis- We conceived the coefficientMakna (http:// umenting the class. The system describes cussions. Furthermore, users can introduce members.deri.at/~elenas/coefficient) system properties similarly. Pages related to in- inconsistencies by provisioning arguments as a wiki-based tool for distributed ontology stances of an ontology additionally include (challenge versus justification) or proposing engineering. The system builds on the light- a reference to the associated classes. contradicting ideas. weight semantic wiki engine Makna (http:// Users can mark specific wiki pages as

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Authorized licensed use limited to: UNIVERSITY OF ALBERTA. Downloaded on October 1, 2009 at 14:32 from IEEE Xplore. Restrictions apply. ontology entities with the help of an Ajax ing recipes—to provide IT systems with new University of Berlin. Fifteen students built a assistant or by using the wiki syntax. They levels of data and process interoperability. community wiki to provide dessert recipe can edit ontology entities in the same way as Some ontologies had initial versions avail- information using semantic technologies. regular wiki articles and add or delete state- able prior to the case study; others were built They used the DILIGENT framework and an ments about these entities using a dedicated from scratch. To reach agreement on the tar- underlying ontology for concept-based search assistant. Users can browse the ontology by get ontology, the participants discussed and for semantic annotation of recipe docu- content or structure, and the core wiki engine domain and application requirements, mod- ments written in German. We supervised the provides versioning support. Users can ex- eling issues, and domain-knowledge for- students during the case study and later ana- port new versions to OWL and RDF Schema malization. Each of the four studies needed lyzed the arguments they exchanged using for further external processing. External rea- additional methodological and technological the system and collected their experiences soning services can help detect inconsisten- support to trace the exchanged arguments with coefficientMakna. We started the case cies at the content level. The DILIGENT argu- and focus the discussions. The problems in study with the following hypotheses: mentation ontology plays an important role a completely distributed environment such in coefficientMakna’s functionality. The sys- as the Semantic Web will likely be much • The students can agree on a shared ontol- tem uses this ontology as a formal descrip- more difficult. ogy and find the guidance given by the tion of the argumentation items relevant to Our first case studies focused on identify- framework useful for this purpose. ontology-engineering discussions: issues, ing and selecting argument types likely to be • Nonexperts don’t require extensive train- ideas, and various argument types. ing to use the argumentation model. • The DILIGENT framework, and thus the ap- Collaboration plication of its argumentation theory to We extended JSPWiki with discussion Users can edit ontology ontology engineering, enhances design pages associated with wiki articles referring decision traceability. to ontological primitives—either domain- entities in the same way • coefficientMakna adequately supports the specific concepts (that is, the collaboratively argumentation process. built ontology) or argumentation items (that as regular wiki articles and is, instances of the DILIGENT argumentation At the beginning of the study, the engi- ontology). Users can mark specific wiki pages add or delete statements neering team, consisting of eight students, as issues for an engineering context and cre- participated in a tutorial on ontology engi- ate and elaborate ideas to solve specific is- about these entities using neering and the DILIGENT argumentation sues. To ease the creation of argumentation- framework. Afterward, they collaboratively specific wiki content on the discussion pages, built several versions of the ontology over the user interface has customized buttons spe- a dedicated assistant. four months. Prior to the requirements analy- cific to the DILIGENT argumentation model sis, they chose an ontology-engineering ex- functionality. pert as a moderator, who supervised the dis- useful in ontology-engineering discussions. cussions and mediated major conflicts. The Externalization and detection We asked participants to agree on an ontol- team decided to use majority voting as an coefficientMakna captures the ontology- ogy to represent the research topics typically instrument to resolve conflicts related to engineering discussions as instances of the addressed in a computer science institute. In modeling decisions. During requirements argumentation ontology: concrete issues, the first experiment, we provided only lim- analysis, the participants first agreed on a ideas, elaborations, and pro and con argu- ited moderation. In the second, we followed minimal list of competency questions the ments. Users can query this data to monitor the framework, albeit without tool support. prospected ontology must answer, and so the status of discussions, progress, and pos- The framework nevertheless let us identify specified an initial set of issues. sible conflicts and to reconstruct the ratio- the relevant arguments and define the col- Discussions were carried out using the nale behind certain decisions. An external laborative ontology development process. In coefficientMakna system—in particular, the reasoning service, which analyzes the for- a subsequent study, we observed legal ex- argumentation model provides the set of pos- malized discussion data, helps detect con- perts using our framework to build an on- sible argument types and a procedure to tradictions and conflicts. tology to represent professional legal knowl- reach consensus on the raised issues. Among edge. In contrast to the first experiment, the other issues, the participants addressed and Case studies participants had no prior expertise in ontol- agreed on the abstractions needed to reduce We conducted several case studies to eval- ogy engineering. The framework enabled the model’s complexity. They decided that uate and refine the DILIGENT framework. We legal-domain experts to formalize their do- the ontology would contain a classification found that direct interaction (through argu- main, and they appreciated its detailed guid- of desserts, food products, and ingredients as ment exchange) is particularly important for ance even though tool support was limited at well as information specific to recipes: prepa- fostering consensus while creating a truly that time. We describe in more detail the ration time, degree of difficulty, region of ori- shared ontology.6 The case studies dealt with results of our last case study, in which par- gin, calories per measuring unit, and required building shared ontologies in various do- ticipants used the framework with the sup- kitchen devices. Additional domain-specific mains—ranging from research topics and port of coefficientMakna. information such as provenance or prepara- legal affairs to tourist information and cook- We performed the case study at the Free tion procedure were considered secondary

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occurred; in particular, questions arose over the cost benefit of reusing classifications and over modeling units of measurement for ingredients. For the former, the moderator needed to intervene; in the latter, the participants reached an agreement. As a result of the case study, the students agreed on a shared ontology and used it as the system’s conceptual backbone. The system is now used to collect and share recipes. In comparison to our previous case studies, the tool significantly sped up the provisioning of arguments and eased the methodology’s application. Because most participants Figure 3. A DILIGENT issue represented as a wiki page in coefficientMakna. were already familiar with basic wiki prin- cipals, they required only a brief introduction to work with the system in addition to the DILIGENT specific training.

ur case studies show that the DILIGENT Oargumentation process guides ontology-engineering discussions and ensures an efficient agreement process. Nonexperts can quickly understand and follow the process. The theory of argumentation and the predef- inition of argument types simplifies the structure of the discussions and facilitates the Figure 4. A discussion page in coefficientMakna. detection of conflicting arguments. At the same time, coefficientMakna significantly for the application setting. ianunwin.demon.co.uk/eurocode/docmn), the reduces the effort to capture the arguments in The corresponding wiki page contains a ISO 3166 country name codes system (www. a structured way. However, our evaluation natural language description of the current niso.org/standards/resources/3166.html), and results are qualitative rather than quantitative. problem (see figure 3). By means of the two the ontology UnitDim (providing definitions Future case studies will focus on achievable icons on the screen’s upper right, users can of physical units and quantities, www.atoapps. time savings following different engineering switch between the wiki article and the dis- nl/ProjectSite2/data/documents/ approaches. They will account for the engi- cussion, which captures the related elabora- 217618013-20041216155627/UnitDim.owl). neered ontologies’ quality and reusability in tions, arguments, positions, and decisions The team discussed whether reusing these terms of time needed by external parties to ordered chronologically. sources would benefit the overall ontology- understand and integrate the constructed Proposing ideas resolves issues. In coeffi- engineering process, given that two of them ontologies in different applications. More- cientMakna, users added new ideas on the required considerable customization efforts. over, an issue that requires further consider- discussion page of the corresponding issue. They agreed to not reuse these sources. Once ation is the system’s usability. Additional Figure 4 shows an example discussion page in the participants completed requirements assistants and patterns for typical argumen- the Lekapidia study. Using the DILIGENT argu- analysis, the discussion continued on topics tation situations could offer further support. mentation framework, it’s possible to add related to conceptualization and formalization The current coefficientMakna is particu- arguments and positions to an idea or make a (how to model or name certain entities, how to larly suited for the developing simple inher- decision on it with the help of the assistants distinguish between classes and properties, itance hierarchies. Native ontology editors located on screen’s upper right. A special and so on). More complex formalizations, such as Protégé better support modeling page shows the argumentation process status. such as those related to range or cardinality axioms and other advanced ontology primi- The system realizes this using a query on the constraints, have also been carried on using tives. With respect to automating the argu- statements added to the discussion pages. the native ontology editor Protégé (http:// mentation process, we found that resolving Later, participants identified potentially rel- protege.stanford.edu). For more complex for- natural language conceptualizations into evant information sources, which could be malizations, coefficientMakna generated an ontology entities is challenging. However, reused in the target recipe ontology: a set of OWL version of the ontology, which the team we foresee that in different ontology-engi- 500 recipes covering the target domain and modified using the ontology editor, prior to neering projects, similar issues will emerge. several general-purpose classifications, such reloading the ontologies to the wiki for fur- It might be possible to detect patterns and as the Eurocode2 Food Coding System (www. ther discussions. Several conflicting situations propose the best modeling solution.

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Authorized licensed use limited to: UNIVERSITY OF ALBERTA. Downloaded on October 1, 2009 at 14:32 from IEEE Xplore. Restrictions apply. Our research shows that argumentation theory has much to offer current Semantic The Authors Web challenges. However, general argumentation frameworks emerging in adjacent Christoph Tempich is a consultant at Detecon International in Compe- disciplines require some customization to fit tence Practice Information Technology group. His research interests include business models for the Semantic Web, Semantic Web technologies and an ontology-engineering scenario’s concrete applications, ontology engineering, and knowledge management. He re- requirements. In the future, we might be able ceived his PhD in applied informatics from the University of Karlsruhe, to apply findings related, for example, to Institute of Applied Informatics and Formal Description Methods (AIFB). negotiation in ontology engineering to fur- He is a member of the TM Forum and the Open Group. Contact him at Dete- ther facilitate building ontologies. con Int’l, Oberkasseler Str. 2, D-53225 Bonn, Germany; tempich@aifb. uni-karlsruhe.de.

References Elena Simperl is the deputy director of the Digital Enterprise Research Institute at the University of Innbsbruck, and the education manager of the 1. A. Gómez-Pérez, M. Fernández-López, and Semantic Technologies Institute International. Her research interests include O. Corcho, “Ontological Engineering,” Ad- semantic technologies, ontology engineering, collaborative applications, and vanced Information and Knowledge Process- business aspects of the Semantic Web. She received her PhD in computer ing, Springer, 2003. science from the Free University of Berlin. Since 2006, she’s organized the Semantic Web PhD Student Network for Berlin and Brandenburg and the 2. C.W. Holsapple and K.D. Joshi, “A Collabo- Knowledge Web PhD Symposium at the European Semantic Web Confer- rative Approach to Ontology Design,” Comm. ence. Contact her at the Digital Enterprise Research Inst., Leopold-Franzens- ACM, vol. 45, no. 2, 2002, pp. 42–47. Univ. Innsbruck, ICT Technologiepark, Technikerstr. 21a, 6020 Innsbruck, Austria; [email protected]. 3. P. McBurney, D. Hitchcock, and S. Parsons, “The Eightfold Way of Deliberation Dia- logue,” Int’l J. Intelligent Systems, vol. 22, no. Markus Luczak is a member of the Networked Information Systems Work- 1, 2007, pp. 95–132. ing Group at the Free University of Berlin. His research interests include Web collaboration, software engineering, ontology engineering, and corpo- 4. W. Kunz and H.W.J. Rittel, Issues as Ele- rate Semantic Webs. He received his diploma in computer science from the ments of Information Systems, working paper Free University of Berlin. Contact him at the Free Univ. of Berlin, Takustr. 131, Inst. Urban and Regional Development, 9, 14195 Berlin, Germany; [email protected]. Univ. of California, 1970.

5. A. Selvin et al., “Compendium: Making Meet- ings into Knowledge Events,” Proc. Knowl- edge Technologies,AKT Technologies, 2001; http://eprints.aktors.org/155. Rudi Studer is a full professor in applied informatics at the University of 6. C. Tempich, “Ontology Engineering and Karlsruhe, Institute of Applied Informatics and Formal Description Methods Routing in Distributed Knowledge Manage- (AIFB). His research interests include knowledge management, Semantic ment Applications,” doctoral dissertation, In- Web technologies and applications, ontology management, text mining, and stitut für Angewandte Informatik und Formale Semantic Web services. He’s a member of the board of the Research Center Beschreibungsverfahren, Univ. Karlsruhe, for Information Technologies (FZI) at the University of Karlsruhe and a 2006. director of the Information Process Engineering research group at the FZI. He cofounded the spin-off company ontoprise GmbH, which develops 7. C. Potts and G. Bruns, “Recording the Rea- semantic applications. He’s an associate editor of the ACM Transactions on sons for Design Decisions,” Proc. 10th Int’l Internet Technology and an advisory board member of IEEE Intelligent Systems. He’s technical direc- Conf. Software Eng.,” IEEE CS Press, 1988, tor of the EU-funded Integrated Project NeOn (Lifecycle Support for Networked Ontologies). Con- pp. 418–427. tact him at the Institut für Angewandte Informatik und Formale Beschreibungsverfahren, Univ. Karl- sruhe, D-76128 Karlsruhe, Germany; [email protected]. 8. M. Uschold and M. Grueninger, “Ontologies, Principles, Methods and Applications,” Knowledge Sharing and Rev., vol. 11, no. 2, 1996, pp. 93–155. H. Sofia Pinto is an assistant professor at the Lisbon Instituto Superior Tec- nico’s Department of Computer Science and Engineering. Her research inter- 9. W.C. Mann and S.A. Thompson, “Rhetorical ests include ontology engineering and ontology use, in general, including ontol- Structure Theory: A Theory of Text Organi- ogy learning, ontology evaluation, semantic annotation, ontology reuse, the zation,” The Structure of Discourse, L. Po- Semantic Web, knowledge engineering, knowledge management, and virtual lanyi, ed., Ablex Publishing, 1987, pp. 85–96. organizations. She received her PhD in computer science and AI from the IST. She coorganized the Building and Applying Ontologies for the Semantic Web 10. I. Rahwan et al., “Argumentation-Based Ne- workshop and has collaborated in several EU projects, such as SWAP, SEKT gotiation,” Knowledge Eng. Rev., vol. 18, no. and NeOn. Contact her at the Inst. Superior Tecnico, Dept. de Eng. Informat- 4, 2003, pp. 343–375. ica, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; [email protected]. 11. A. Souzis, “Building a Semantic Wiki,” IEEE Intelligent Systems, vol. 20, no. 5, 2005, pp. 87–91.

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