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Managing multilingual vocabularies and ontologies


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Managing multilingual vocabularies and ontologies

  1. 1. Modeling and Linked Data Publishing of multilingual KOSs in the Agricultural Domain Mauro Dragoni Fondazione Bruno Kessler (FBK) Shape and Evolve Living Knowledge Unit (SHELL) - Organic.Lingua Closure Workshop, Rome, Italy – February 6th, 2014
  2. 2. Background  Knowledge Organization Systems  Knowledge Organization Systems (KOSs), encompass all types of schemes for organizing information and promoting knowledge management.  They have been introduced to reduce the ambiguity of natural language when describing and retrieving information.  KOSs include various classification types & schemes (Glossaries, Taxonomies, Thesauri, Classifications, Ontologies).
  3. 3. The importance of KOS  … in general   A shared and formal interpretation of the domain.  Solve ambiguities;   Common dictionary of terms. 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 Language Expert Language Expert MoKi Knowledge Engineer Modelling tool the Modelling W iKi --- Domain expert Translation Services Domain expert Knowledge Engineer
  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 o o o o The connection with three different machine translation services. An ontology enrichment service for retrieving suggestions about new concepts based on the analysis of external textual resources. A mapping suggestion service allowing the creation of linking between the Organic.Lingua ontology and external ontologies. The support for entity evolution workflow in order to control the update of each entity defined in the Organic.Lingua ontology. 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