Multilingual Knowledge Organization Systems Management: Best Practices

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This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems.
Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi.

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  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • The modelling wiki should provided different views or access modalities to support the different users in accessing the models and contributing to the mdoelling activities in the waythat better suit their modelling skills. While for the informal part, a single general modalities may be foreseen, a domain expert may access the formal part in a moality customized to his/her modelling skill and capabilities, for example throught textual forms which emphasize the main properties of the element, while the KE may access the same knwoeldge through a modality which allows him/her to deal with the full expressivity of the language considered. The important thing to know is that the access modalities refers to the same content, customized in different way.
  • Here’s the outline of the talk is the following. First I will present the formal model that we propose to represent ontolgoies and procedural knwoledge, then I will describe the architecture that we designed to support collconc model in wikis, and final I will present you MoKi and some of its usages in “real” situations.
  • In wikis, the basic entity is the page. Therefore, we propose to represent the basic entities of the languages considered, ontologies and processes,
  • In wikis, the basic entity is the page. Therefore, we propose to represent the basic entities of the languages considered, ontologies and processes,
  • In wikis, the basic entity is the page. Therefore, we propose to represent the basic entities of the languages considered, ontologies and processes,
  • Multilingual Knowledge Organization Systems Management: Best Practices

    1. 1. Modeling and Management of multilingual KOSs in the Agricultural Domain Mauro Dragoni Fondazione Bruno Kessler (FBK) Shape and Evolve Living Knowledge Unit (SHELL) https://shell.fbk.eu/index.php/Mauro_Dragoni - dragoni@fbk.eu Organic.Lingua Best Practices and Tools for Multilingual Online Services Workshop, Webinar – March 27th, 2014
    2. 2. Background  Knowledge Organization Systems  Knowledge Organization Systems (KOSs), encompass all types of schemes for organizing information and promoting knowledge management.  KOSs include various classification types & schemes (Glossaries, Taxonomies, Thesauri, Classifications, Ontologies).  They have been introduced to reduce the ambiguity of natural language when describing and retrieving information.
    3. 3. The importance of KOS  … in general  Common dictionary of terms.  A shared and formal interpretation of the domain.  Solve ambiguities;  Share knowledge (not only between humans, but also between machines)  … in Information Access  Different people may use different words for the same concept or employ different concepts to refer to the same scientific terms.  KOSs may improve access to (mostly digitally) stored information, by offering structured ways to represent and model it.
    4. 4. Challenges  Evolution of existing KOSs (revisions, additions/deletions of concepts);  Multilinguality issues (KOSs available in multiple languages);  Collaborative working (groups of experts based in different countries with different competencies working on the same tasks/scenarios).  Exposure of the modeled information and linking with third-party KOSs
    5. 5. Evolution  Changes in conceptualization  Increasing/decreasing the granularity of the representation  Knowledge enrichment
    6. 6. Multilinguality  Break the language barriers  Make contents accessible transparently with respect to the language  Increasing the capability of sharing knowledge
    7. 7. Collaboration
    8. 8. Exposure and Linking  Make knowledge consumable by third-party services  Avoiding the replication of the knowledge  Contextualize the modeled knowledge within the domain
    9. 9. An architecture for collaborative modeling, evolution, and exposure of multilingual KOSs
    10. 10. MoKi basic features  It is based on the use of wikis  It implements different views built based on the expertise of different users  It stores information both in unstructured and structured ways
    11. 11. Different views for different roles
    12. 12. Support for multilingual ontology management Modelling tool MoKithe Modelling WiKi --- Domain expert Knowledge Engineer Knowledge Engineer Domain expert Translation Services Language Expert Language Expert
    13. 13. Connections with external components and data exposure  Automatic suggestions of mappings between ontologies  Managing ontology evolution  Entity workflow management  Exposure service in different formats (OWL, SKOS, …)  Terms/Concepts extractor  …
    14. 14. What happened in the Organic.Lingua project  What does Organic.Lingua MoKi include? o The connection with three different machine translation services. o An ontology enrichment service for retrieving suggestions about new concepts based on the analysis of external textual resources. o A mapping suggestion service allowing the creation of linking between the Organic.Lingua ontology and external ontologies. o The support for entity evolution workflow in order to control the update of each entity defined in the Organic.Lingua ontology. o An ontology service providing an API Rest Service for the exposure of the ontology in Linked Open Data
    15. 15. What happened in the Organic.Lingua project  How the Organic.Lingua ontology evolved during the project: o entity creation: 54 o entity update: 2160 o entity deletion: 25 o entity translation: 2136 o discussion creation: 218 o discussion update: 492 o 2 languages added: Italian and Latvian o description added for all languages o 260 concepts have been mapped with AGROVOC
    16. 16. Mauro Dragoni https://shell.fbk.eu/index.php/Mauro_Dragoni dragoni@fbk.eu

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