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Kasenchak "What Is Semantic Search? And Why Is It Important?"

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This presentation was provided by Bob Kasenchak of Access Innovations, during the NISO Webinar "Discover and Online Search, Part Two: Personalized Content, Personal Data," which was held on June 19, 2019.

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Kasenchak "What Is Semantic Search? And Why Is It Important?"

  1. 1. What Is Semantic Search? And Why Is It Important? Bob Kasenchak Access Innovations @taxobob NISO Webinar “Discovery and Online search: Personalized Content, Personal Data”
  2. 2. Outline Semantic Search ● What Is It? (Basics) ● Why Do We Need It? (Why Does Search Fail?) ● So…What Is It? (Specifics) ● Examples and Implementations
  3. 3. What Is Semantic Search? Semantic Search goes beyond keyword searches to examine Context Google says “Things Not Strings”
  4. 4. Why “Basic” Search Fails Why does search fail? ● Simple search simply matches text strings ● Language is ambiguous ● There is a *lot* of content
  5. 5. Discovery Google Scholar
  6. 6. Discovery
  7. 7. Specialized Repositories Why “Basic” Search Fails
  8. 8. Specialized Repositories Discovery
  9. 9. Why “Basic” Search Fails Search fails because simple string matching is not adequate for large, specialized repositories of content with technical language that evolves over time. (Also, language is ambiguous.)
  10. 10. What Is Semantic Search? Semantic Search goes beyond keyword string-matching to examine Context using a variety of means Google says “Things Not Strings”
  11. 11. What Is Semantic Search? Semantic Search Examines the semantic context of the search query to drive relevant results. This can include: taxonomies, lexical variants, location, your previous searches, previous similar searches, ontologies, knowledge graphs, and other strategies.
  12. 12. Allow Lexical Variants “Fuzzy Matching” and Similar Techniques ● Use Levenshtein distance (or similar) to match misspellings and variants ● Stem words for search ● Instead of exact string matches ● This can cause noise, be careful!
  13. 13. Query Parsing
  14. 14. Contextual Search: Location
  15. 15. Contextual Search: User Activity
  16. 16. Contextual Search: User Activity
  17. 17. Google Knowledge Graph Google (again): Things not Strings The Google Knowledge Graph connects search with e.g. known facts about entities (Driven by a big old ontology)
  18. 18. Google Knowledge Graph
  19. 19. Google Knowledge Graph
  20. 20. Google Knowledge Graph
  21. 21. Taxonomies and Tagging Controlling Vocabularies ● Search tags before free text (search engine tuning) ● Allow users to browse (in addition to querying) ● Suggest topics using type-ahead or “did you mean” ● Leverage synonymy to deliver same relevant results from various inputs
  22. 22. Taxonomies and Tagging The Irony of Document Categorization ● We’re interested in concepts ● Words are ambiguous ● But words in the text are all we have to go on ○ Unless we apply good subject metadata
  23. 23. PLOS ● 9000+ term thesaurus ● And ~4000 Synonyms (!) ● Applied to documents ● Exposed in browse (!) ● Used to redirect search queries for synonyms ● Exposed at article level to user ○ Crowdsourced QC! Taxonomies and Tagging
  24. 24. Discovery PLOS
  25. 25. Discovery PLOS
  26. 26. JSTOR ● Document becomes the search query (!) ● Combination of taxonomy and naive classification ● Suggests related content for research, bibliography ● Experimental, successful, also very cool Other Novel Approaches
  27. 27. Discovery JSTOR
  28. 28. ● Simple things: ● Using existing search software tuning/options ● Enable fuzzy matching ● Configure how Booleans are automatically applied ● Weight fields, doc types, etc. where appropriate ● Use dates to deliver recent results Implementation: Kind of Easy
  29. 29. ● Next level: ● Taxonomy ● And tagging ● Knowledge Graphs ● User profiles, user behavior, other targeted means Implementation: More Complex
  30. 30. Thanks! Bob Kasenchak Access Innovations @taxobob NISO Webinar “Discovery and Online search: Personalized Content, Personal Data”