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Yuri de Lugt Collexis Karin Clavel TU Delft Library

Yuri de Lugt Collexis Karin Clavel TU Delft Library. The Collexis Company. Collexis develops and implements software for making large amounts of (un)structured data easily accessible Founded in 1999, 40 employees Based in USA, NL, GE sales/development in the Netherlands (Geldermalsen)

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Yuri de Lugt Collexis Karin Clavel TU Delft Library

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  1. Yuri de Lugt Collexis Karin Clavel TU Delft Library

  2. The Collexis Company • Collexis develops and implements software for making large amounts of (un)structured data easily accessible • Founded in 1999, 40 employees • Based in USA, NL, GE • sales/development in the Netherlands (Geldermalsen) • Worldwide coverage through partnerships • Collexis grants free licenses to selected projects in developing countries

  3. Customer in Library domain

  4. Introduction • The main question: “Is semantic & concept search a competitive Edge?” “The role of the expert, trivial?” • Collexis introduced • Case: University Library of Wageningen • Case: University Library of Delft

  5. Search Engine vs. Knowledge Engine

  6. Elementary principle: Validation • Validation of: • Content • Source • Concept • Meaning and of interpretation How….? By expert involvement !! • Domain restricted • Targeted Content sources

  7. The Thesaurus – The Expert • A thesaurus defines the world that we are looking at • Domain experts’ expertise is used to create a thesaurus: therefore a thesaurus is validated knowledge • Every user benefits from the knowledge the expert added to the thesaurus • Natural language is very complex; a thesaurus helps us to ‘understand’ the natural language • Find, purchase or build => validated by experts

  8. 3 KnowledgeDiscovery association link information in a document collection Explore beyond existing knowledge Metadata aggregation 2 aggregation/clustering combine information per meta data and subset of documents combine information per subset of documents Ease of use, Ease of Search, Validated results 1 document - based information retrieval identify relevant terms in documents/query identify relevant terms in documents/query Levels of Ambition

  9. Added value for Library • Collexis exactly knows what text is all about • including homonyms, synonyms, multi-lingual aspects, Hierarchy knowledge • Search and retrieval made easy • Multiple search phrases, Combining of sources, Concept-based search, Classification/keyword tagging • Using the existing information • Finding experts, Knowledge actively used within the curriculum, Detection of plagiarism

  10. Case: Wageningen UR Library • Sources: • Article and Experts • Search on: • Content and Metadata • Ease of use, Ease of Search • Deep linking to: • Repository • Yellow Pages (WaY)

  11. Definitions • Information retrieval (IR) the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within (hypertext-)databases for text, sound, images or data. • Knowledge Management (KM) refers to a range of practices used by organizations to identify, create, represent, and distribute knowledge for reuse and learning across the organization Gathering the information Doing something useful with it From: www.wikipedia.org

  12. The main Questions • “Is semantic & concept search a competitive Edge?” • “The role of the expert, trivial?”

  13. The Case: TU Delft Library • http://tulib.library.tudelft.nl • Goal:Online Information Literacy Instruction that students will actually use: • Interactive • Intuitive • Fun • What you see is what you get

  14. Demonstration Wednesday, October 8, 2014

  15. Online user survey • 12 respondents so far… • Students (mostly BSc) and Professors • 9/12 succeed a specific task using the Tag Cloud Search • 8/12 like using it • 8/12 think it’s useful

  16. Future plans • Can the Tag Cloud Search completely replace the traditional menu? • Improve thesaurus based on user behaviour

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