Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet
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Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet

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The objective of this webinar is to provide a brief overview of the Knowledge Organization Systems (KOS) and the tools used for managing them. The presentation will focus on the management of the ...

The objective of this webinar is to provide a brief overview of the Knowledge Organization Systems (KOS) and the tools used for managing them. The presentation will focus on the management of the multilingual Organic.Edunet ontology as a case study. In this context it will present aspects such as the collaborative work, multilinguality needs and update of the concepts using an online KOS management tool (MoKi).

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  • Image source: http://en.wikipedia.org/wiki/File:TopicMapKeyConcepts2.PNG
  • Classification based on http://www.clir.org/pubs/reports/pub91/1knowledge.html
  • Image taken from Plant Ontology
  • Source: http://www.slideshare.net/Agro-Know/managing-multilingual-vocabularies-and-ontologies

Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet Presentation Transcript

  • Webinars@AIMS 21/2/2014 Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet” Vassilis Protonotarios, Agricultural Biotechnologist, PhD Agro-Know, Greece / University of Alcalá, Spain
  • Contents of the presentation  (Short) introduction to KOS  Open source KOS management tools  The MoKi tool  The Organic.Edunet ontology  Using MoKi for managing the Organic.Edunet ontology  Next steps
  • Introduction to KOS
  • About KOS KOS = Knowledge Organization Systems ◦ a generic term used in knowledge organization including the following types ◦ Term lists  Authority Files  Glossaries  Dictionaries • Relationship Lists • Thesauri • Semantic Networks • Topic maps • ◦ Classifications & Categories Ontologies  Subject Headings
  • Focusing on ontologies  Ontology: Explicit formal specification of terms in a domain AND the relations among them
  • Tree-view of an ontology
  • But why use KOSs? A standardized mean for referring to the same concept using a unique name  A mean for the classification of different resources in a domain  …and of course the backbone of linking heterogeneous data sources 
  • Open Source KOS Management tools (indicative list)
  • Talking about KOS management  Manage entries ◦ Add, revise, delete Translate entries  Change relationships  Import existing lists of terms/concepts  Export the lists as OWL/SKOS 
  • Tools: VocBench available at http://vocbench.uniroma2.it/  developed by FAO and its partners;  a web-based, multilingual, editing and workflow tool;  manages thesauri, authority lists and glossaries using SKOS;  facilitates the collaborative editing of multilingual terminology and semantic concept information. 
  • VocBench screenshot
  • Tools: Protégé      Available at http://protege.stanford.edu developed by the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine; ontology editor and knowledge-base framework; supports modeling ontologies via a web client or a desktop client; Protégé ontologies can be developed in a variety of formats including OWL, RDF(S), and XML Schema
  • Protégé screenshot
  • Tools: TemaTres Available at http://www.vocabularyserver.com  a tool for the development & management of       controlled vocabularies, thesauri, taxonomies other types of formal representations of knowledge ensures consistency & integrity of data and relationships between terms
  • TemaTres screenshot
  • Tools: Neologism     Available at: http://neologism.deri.ie/ developed by DERI (Digital Enterprise Research Institute), Ireland a vocabulary publishing platform for the Web of Data focuses on ease of use and compatibility with Linked Data principles ◦ facilitates the creation of RDF classes and properties   supports the RDFS standard, and a part of OWL Is NOT ontology/SKOS editor and does not support multilingual labels
  • Neologism screenshot
  • The MoKi tool
  • MoKi: the Enterprise Modelling WiKi  Available at https://moki.fbk.eu/website/index.php Developed by FBK, Italy  Supports the construction of integrated domain & process models  Easy editing of a wiki page by means of forms  Automatic import and export in OWL and BPMN 
  • MoKi screenshot (2011)
  • MoKi evolution  During the Organic.Lingua ICT/PSP project: ◦ Multilinguality options  Integration of three machine translation services ◦ Ontology enrichment services  Automatically suggests new concepts for the ontology ◦ Mapping component  Used for mapping the OE ontology to AGROVOC ◦ Collaboration options  Decisions made on discussions ◦ Ontology service  Exposure of ontology through REST API
  • The Organic.Edunet ontology
  • The Organic.Edunet ontology a conceptual model useful for classifying learning materials on the Organic Agriculture (OA) and Agroecology (AE) domain  Developed in the context of the Organic.Edunet eContentPlus project  Used by Organic.Edunet for the classification of educational resources 
  • The Organic.Edunet ontology  Currently consists of 381 concepts translated in 18 languages
  • Translating the OE ontology (2010)
  • Building the Organic.Edunet ontology (1/3) OA & AE domain experts elaborated a list including all the relevant terms in the domain of OA & AE Using the list of terms as input, domain experts identified subdomains with the aim of dividing the original list into microthesauri 1. 2. ◦ with the help of librarians and guidance from the ontology experts
  • Building the Organic.Edunet ontology (2/3) 3. 4. 5. Domain experts added agreed, unambiguous definitions for the terms, thus producing a “concept list” Ontology experts developed an initial ontology from the concept list The ontology produced in the previous step was evaluated making use of upper ontologies
  • Building the Organic.Edunet ontology (3/3)
  • Evolution of the Organic.Edunet ontology using MoKi
  • Time for evolution  Organic.Lingua ICT-PSP project (20112014) ◦ Aims to enhance the multilinguality options of the Organic.Edunet Web portal ◦ provided the opportunity for updating & revising the Organic.Edunet ontology
  • The requirements  Multilinguality ◦ Facilitate the translation processes  Avoid using spreadsheets for translations  Use of machine translation tools ◦ Automate process  Collaborative work ◦ Use web-based tool ◦ Enable discussions for concept revisions ◦ Enable different translations to take place at the same time  Exposure ◦ Automatic exposure of the ontology through API
  • The process (1/2)  Formation of teams ◦ Ontology experts / knowledge engineers ◦ Domain experts ◦ Language experts  Definition of tasks ◦ Deprecation of less-frequently used concepts ◦ Refinement of most widely-used concepts ◦ Addition of new concepts ◦ Translation of concepts
  • The process (2/2)  Development of scenarios ◦ A number of scenarios was developed per task & with specific deadlines  Collaborative work ◦ Discussions in MoKi ◦ Evolution based on discussions ◦ Validation of revisions by experts
  • Discussions in MoKi
  • Concept management  Refers to  Editing concept  Renaming concept  Deleting concept
  • Introduction of new concepts  Ontology suggestion service     Verified Keywords, User (modified) Keywords, (Automatically) Extracted Keywords and Search-Query-Logs
  • Translation of concepts (2013)
  • Mapping to AGROVOC
  • Exposure of concepts Ontology service = use of API http://wiki.organiclingua.eu/APIs#Ontology_Service_API  ◦ Publish/expose the ontology ◦ Enable up-to-date publishing  Two different interfaces: ◦ Linked Open Data (LOD): Provides data in SKOS format ◦ RESTful RDF: Exposes data in OWL2 or LOD format
  • Case study: the use of the ontology service API
  • OE ontology evolution in numbers
  • Next steps
  • Next steps in the ontology evolution (1/2)  Further work on the concepts ◦ ◦ ◦ ◦ Introduction of new concepts Refinement of existing ones Deprecation of existing ones Translation of concepts in additional languages ◦ Mapping of the ontology to additional ones
  • Next steps in the ontology evolution (2/2)  Publication of ontology as linked data ◦ Definition of a namespace ◦ Ensure compliance with existing standards  Link ontology with other related ones ◦ Already linked to AGROVOC
  • Acknowledgements The Organic.Edunet ontology was developed in the context of the Organic.Edunet project under the eContentPlus Programme  Parts of the work described in this presentation were partially funded by the Organic.Lingua project under the ICT Policy Support Programme 
  • www.organic-lingua.eu Contact me at: vprot@agroknow.gr Thank you!