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A Pattern-Based Ontology Engineering Platform Thomas Janke SAP Research, Dresden, Germany thomas.janke@sap.com Abstract: Building knowledge management systems based on semantic technologies can pr<strong>of</strong>it from <strong>the</strong> use <strong>of</strong> ontologies. The latter provide a shared vocabulary, which helps to shape common understanding, fosters reuse and data integration as well as facilitates reasoning. The problem companies are facing, though, is that, despite all advantages, ontology engineering is very hard to master. First <strong>of</strong> all, domain engineers need to select <strong>the</strong> most suitable ontology language from <strong>the</strong> pool <strong>of</strong> available languages ensuring that <strong>the</strong> chosen language reflects <strong>the</strong> requirements <strong>of</strong> <strong>the</strong> given domain by means <strong>of</strong> expressivity, reasoning performance and tool support. Moreover, in order to model high-quality ontologies, a good knowledge about <strong>the</strong> various logical formalisms, <strong>the</strong> <strong>of</strong>fered ontology languages are based on, <strong>the</strong>ir characteristics and <strong>the</strong>ir differences is required. Published best practices for ontology engineering, so called Ontology Design Patterns (ODPs) (Gangemi and Presutti, 2009) can help here. They describe how to solve common modeling problems. The problem, though, is that in order to apply such a pattern, ontology engineers not only have to find a suitable pattern but also need to understand its semantics. The platform presented in this paper tackles those problems from two directions. First <strong>of</strong> all, domain engineers are enabled to model <strong>the</strong>ir domain utilizing domain-specific languages reflecting <strong>the</strong> specific requirements <strong>of</strong> <strong>the</strong> particular domain. As a result, <strong>the</strong> modeling task will be significantly simplified. Moreover, based on <strong>the</strong> introduced level <strong>of</strong> abstraction, <strong>the</strong> platform will enable <strong>the</strong> generation <strong>of</strong> concrete ontologies in various target languages and in doing so, mitigates <strong>the</strong> problem <strong>of</strong> language selection. Fur<strong>the</strong>rmore, <strong>the</strong> ontology generation component introduces room for <strong>the</strong> second main principle which is <strong>the</strong> automatic and transparent application <strong>of</strong> ODPs. This means that every transformation <strong>of</strong> domain-specific models into ontologies is based on executable ontology patterns which can be contributed and registered to <strong>the</strong> platform by ontology experts. Keywords: Ontology engineering, Model driven development, Ontology design patterns, Domain-specific languages 1. Background Knowledge management can be facilitated by utilizing semantic technologies. The reasons for that are manifold. First <strong>of</strong> all, ontologies provide a vocabulary <strong>of</strong> domain relevant terms which helps to build a common understanding. Moreover, <strong>the</strong>y can be designed in a modular way which fosters reuse, helps to build standard vocabularies for certain domains (e.g. Friend <strong>of</strong> a Friend, Dublin Core) and provides a basis for data integration. In addition, ontologies allow for inferring implicit knowledge by means <strong>of</strong> reasoning. As a result, relevant parts <strong>of</strong> <strong>the</strong> knowledge in a specific domain can be explicitly modelled in <strong>the</strong> ontology, in contrast to implementing <strong>the</strong> required logic in <strong>the</strong> business logic layer <strong>of</strong> a target application. The problem, however, is that ontology engineering is challenging, especially for non-experts. This problem has many dimensions. First <strong>of</strong> all, <strong>the</strong>re is <strong>the</strong> aspect <strong>of</strong> language selection. In order to choose <strong>the</strong> ontology language best suited for a given domain, available languages such as RDF(S), OWL 2 with all its flavours and pr<strong>of</strong>iles, Topic Maps, F-Logic, etc., need to be compared with respect to <strong>the</strong>ir expressivity, <strong>the</strong>ir reasoning performance, available frameworks, editing support and so on. Moreover, if <strong>the</strong> requirements for <strong>the</strong> domain model change, <strong>the</strong> process <strong>of</strong> evaluating all <strong>the</strong> languages needs to be repeated. This is not only time consuming but requires deep knowledge about all <strong>the</strong> languages. After selecting a language, <strong>the</strong> next problem emerges and this is <strong>the</strong> lack <strong>of</strong> modeling expertise. The various languages are based on different logical formalisms, starting from description logics and semantic networks to rule based languages. Each formalism has its specific characteristics, for example <strong>the</strong> existence <strong>of</strong> <strong>the</strong> unique name assumption or <strong>the</strong> open world assumption. Knowing about those differences and <strong>the</strong>ir impact is a key requirement to model good ontologies. It addition to that, it is favourable to follow published and widely accepted modeling best practices, in order to guarantee high modeling quality. Referring to <strong>the</strong> related work in this area, those best practices are called ontology design patterns. They describe how to solve common modeling problems and cover experiences gained by ontology engineering experts. The problem, though, is that in order to apply those pattern, domain engineers have to be aware <strong>of</strong> <strong>the</strong>ir existence, <strong>the</strong>y need to find a matching pattern for <strong>the</strong>ir problem, need to understand <strong>the</strong> provided solution and finally have to adopt <strong>the</strong> solution to <strong>the</strong>ir own domain models. As a non-ontology export, each <strong>of</strong> those steps represents a tough challenge. At <strong>the</strong> moment, <strong>the</strong> gap between domain engineers, knowing about <strong>the</strong> particular domain, and ontology engineers, knowing how to transfer <strong>the</strong> modeling requirements into concrete ontologies, can only be filled by an intense collaboration between both roles. This introduces 461
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Abdallah Al-Shawabkeh, Alexander K<
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Table 1: Inn-KM approach contents 2
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Knowledge Dynamics and Organisation
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Eckhard Ammann We distinguish three
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Eckhard Ammann quality dimension ar
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Eckhard Ammann The second cycle, Cy
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Figure 7: Supervised learning-by-do
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Identifying and Ranking the
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Manouchehr Ansari et al users <stro
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2.5 leadership and strategy Manouch
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Manouchehr Ansari et al conducted s
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Serious Games in the</stron
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Seyed Esmaeil Asgharpour and Gholam
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Attitude of Senior
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4.5 Comparative Advantage Seyed Esm
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Contextual Adaptive Visualization E
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2.4 Rights transfer Simona-Clara B
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A Framework for the</strong
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Leila Beig et al Additionally, Wei
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Leila Beig et al The proposed KM au
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Leila Beig et al source context Ele
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Leila Beig et al Figure 1: Learning
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Leila Beig et al Figure 3 visually
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Leila Beig et al based on collectiv
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Experiential Knowledge Creation Pro
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Didiosky Benítez et al Figure 1: E
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Pavel Bogolyubov Figure 1: number <
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4. Results and discussion Pavel Bog
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Pavel Bogolyubov Knowledge Manageme
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Pavel Bogolyubov Table 3: Countries
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Pavel Bogolyubov McNamee, R. C., N.
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Ettore Bolisani, Francesca Gambarot
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Table 2: Synthesis
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Ettore Bolisani, Marco Paiola and E
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Strategies for Increasing Knowledge
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Klaus Bredl, Amrei Groß and Jane F
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Camelia Burja and Vasile Burja Appr
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∀ , i ≥ 0 v u i where: ui repre
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Camelia Burja and Vasile Burja Jack
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Adriana Schiopoiu Burlea With respe
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Adriana Schiopoiu Burlea 3. ESF rol
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Adriana Schiopoiu Burlea In <strong
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3.2 Discussions Adriana Schiopoiu B
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Adriana Schiopoiu Burlea Nonaka, I.
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Simon Cadez and Vlado Dimovski It i
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Why Should I Share my new ideas? Cu
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Francesco Calza, Rossella Canestrin
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National Knowledge Management Strat
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Behiye Çavuşoğlu and Mustafa Sag
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Implementing a work-life balance cu
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Juan-Gabriel Cegarra-Navarro, Mª E
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The Influence of R
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Huei-Fang Chen and Yi-Wen Lin outco
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3.2.4 Knowledge-sharing Huei-Fang C
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Huei-Fang Chen and Yi-Wen Lin that
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Huei-Fang Chen and Yi-Wen Lin Hair,
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2.1.2 Social capital Marguerite Cro
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Marguerite Cronk 2.3 Social capital
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An Exploratory Study of</st
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Françoise de Viron, Thomas Lederer
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4.1.2 The researchers Françoise de
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The Application of
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Nasser Easa and Robin Fincham knowl
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Network Management as a way to Mana
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Eva Eckenhofer An
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Initiation Configuration Interessem
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The Essence of Kno
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Emmanuel Innocents Edoun and Valden
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4. Case study and analysis Emmanuel
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Jamal El-Den and Xin Zhou This rese
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References Tiit Elenurm Accorsi, F.
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Knowledge Workers: A Typology Frame
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Jiří Franek and Eva Grublova crea
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Middle Managers’ Maturity <strong
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Charles Gagné et al attributable t
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Adoption of Knowle
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1.1.2 SMEs and KMS Tendayi Gondo an
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4. What a biofeedb
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References Manel González-Piñero
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Nebojsa Graca and Ana Lucija Gojako
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creating new, added, essential qual
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Receive suggestions Compare suggest
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Opinion of colleag
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Individual Level Influencers on Tac
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Why does knowledge transfer process
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Understanding Personal Knowledge De
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Markus Haag and Yanqing Duan In ord
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Intellectual Capital in Universitie
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Facilitating Knowledge Sharing in V
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