SWT Lecture Session 7 - Advanced uses of RDFS

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SWT Lecture Session 7 - Advanced uses of RDFS

  1. 1. + Advanced use RDFS Mariano Rodriguez-Muro, Free University of Bozen-Bolzano
  2. 2. + Disclaimer  License   A few examples from these slides has been taken from   This work is licensed under a Creative Commons Attribution-Share Alike 3.0 License (http://creativecommons.org/licenses/by-sa/3.0/) Semantic Web for the working Ontologist. Chapter 6. Some of the slides on the use of taxonomies are based on:  http://info.earley.com/webinar-replay-business-value-taxonomyaug-2012
  3. 3. + Reading material  Semantic Web for the working Ontologist. Chapter 6 http://proquest.safaribooksonline.com/book/-/9780123859655
  4. 4. + Uses of RDFS (and ontology)  Application oriented uses     Application behavior without coding Data integration through vocabulary alignment, integration Controlled vocabularies Formal ontology:  Definition of taxonomies, e.g., parent/broader, child/narrower, etc. Taxonomy/Ontology can be used to create business/data value Taxonomy can open the door for new kinds of data management
  5. 5. + Enterprise
  6. 6. + Content Management  Increase the control/productivity that the enterprise has over their data to increase internal productivity, customer satisfaction, etc.  Why not “just Google” your sites? These do not work in the enterprise    Back links and Statistics In the enterprise, granularity is small
  7. 7. + Search enhancement  Search enhancement    Examples:     Finding content (DB., entries, document collections, etc) relevant to a query, but tagged with an alternative name Key is search by metadata and organized metadata Add synonyms to a query Language/translation Include more general terms Precision vs Recall. The focus here is recall, get all “relevant” content.
  8. 8. + Browsing and Navigation: Search overload User doesn’t know what he wants precisely
  9. 9. + Browsing and Navigation: Search overload Facets Give control to the user
  10. 10. + Browsing and Navigation: Search overload Note: Taxonomy is not navigation
  11. 11. + Browsing and navigation, results  Faceted navigation in e-commerce:  Findability  Conversions  Sales  Market size  Customer satisfaction  etc.  Studies show that faceted navigation in enterprise content easily increases all these aspects in hard benchmarks.  See presentation by Earley & Associates
  12. 12. + Content Reuse – Taxonomy in Content Management  Many use cases   Examples, knowledge management, content finding, etc.   Look at business processes, group at targeted users Useful when knowledge is large, and it needs to be accessible fast A Taxonomy can be used to  Define content and document types (e.g., “Article”)  Define the fields that will describe attributes (e.g., tag a document with “Industry”)  Define the actual values of certain fields (e.g., the list of values for the attribute “Industry” might include “Construction”, “Information Technology”, “Utilities”, etc.)
  13. 13. + Example: Knowledge management  Portal development  Service a functional organization, e.g., call centers, technical field services  Key: Changing content  Requires: Access to the latest's and best value always Call centers representatives required 50% less time to solve a problem with correctly organized information. Earley & Associates, 2012 Average reactive time per incident: 10.35hrs Knowledge Helpful Average Reactive TPI: 5.45hrs Knowledge Helpful Time Saved Per Incident: 43%
  14. 14. + Content reuse: Improved Management of Marketing Assets  Type: Magazine Ads  Channel: Print  Target Demographic: Parents  Country: US  Language: English  Concept: Rebellion  Brand: Settletra  Do your kids:  Have discipline problems?  Trouble paying attention?  Trouble getting along? Maybe It’s time to findout how Settletra can help
  15. 15. + Content reuse
  16. 16. + Content reuse: result  Requirement   Question   Do we have material for this campaign No?   Images for campaigns Produce new material Use taxonomies to improve search  $1.25M /yr through digital asset management and increased image reuse (Earley & Associates)
  17. 17. + Public Entities
  18. 18. + The power of large, curated taxonomies  Many large taxonomies developed in the context of large national and international projects  Large amount of knowledge  Clean knowledge (manually curated)  General knowledge (cover domains rather than applications)  Reusable to provide valuable services
  19. 19. + Taxonomies resources  Taxonomy resources:  http://www.taxonomywarehouse.com/  http://www.taxobank.org/  http://www.taxotips.com/resources/sources/  http://id.loc.gov/  http://id.loc.gov/authorities/subjects/sh85112348.html  http://bioportal.bioontology.org/  http://www.w3.org/2001/sw/wiki/SKOS/Datasets Some of these are actually ONTOLOGY repositories
  20. 20. + Taxonomies in Biology  Taxonomies in Biology have been developed for a long time    Large investment world wide Deployed in applications today Include wide range of Biology subjects   Medical terminologies   Macro and Micro biology (Genes, Human Anatomy) Etc. Started as knowledge management/sharing, now applications are being built.
  21. 21. • • • • Download Traverse Search Comment Mapping Services • • • Create Download Upload Widgets • • • Tree-view Auto-complete Graph-view Ontology Services http://rest.bioontology.org Views Annotation Term recognition Data Access Fetch “data” annotated with a given term http://bioportal.bioontology.org
  22. 22. Annotation service Process textual metadata to automatically tag text with as many ontology terms as possible. 90 million calls, ~700 GB of data
  23. 23. Annotating Clinical Text
  24. 24. Resource index Pubmed Abstracts Adverse Events (AERS) GEO : Clinical Trials Drug Bank
  25. 25. + Semantic Annotation services  Semantic Annotations services are very wide spread  Topics include:  General purpose (based on DBPedia URI’s for example)  Specialized  Music  Movies  Libraries  Biology  http://lmgtfy.com/?q=semantic+annotator  http://dbpedia-spotlight.github.com/demo/index.html
  26. 26. + Summary
  27. 27. + Summary: RDFS  Ontology languages, Ontologies  RDFS language and inferences  Common use patterns of RDFS inferences  Taxonomies  Their value and use in the enterprise  Collections and applications

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