1. Evidence from Metadata LBSC 796/CMSC 828o Session 6 – March 1, 2004 Douglas W. Oard

2. Agenda Questions Controlled vocabulary retrieval Generating metadata Metadata standards Putting the pieces together

3. Supporting the Search Process Source Selection Search Query Selection Ranked List Examination Document Delivery Document Query Formulation IR System Indexing Index Acquisition Collection     

4. Problems with “Free Text” Search Homonymy –Terms may have many unrelated meanings –Polysemy (related meanings) is less of a problem Synonymy –Many ways of saying (nearly) the same thing Anaphora –Alternate ways of referring to the same thing

5. Behavior Helps, But not Enough Privacy limits access to observations Queries based on behavior are hard to craft –Explicit queries are rarely used –Query by example requires behavior history “Cold start” problem limits applicability

6. A “Solution:” Concept Retrieval Develop a concept inventory –Uniquely identify concepts using “descriptors” –Concept labels form a “controlled vocabulary” –Organize concepts using a “thesaurus” Assign concept descriptors to documents –Known as “indexing” Craft queries using the controlled vocabulary

7. Two Ways of Searching Write the document using terms to convey meaning Author Content-Based Query-Document Matching Document Terms Query Terms Construct query from terms that may appear in documents Free-Text Searcher Retrieval Status Value Construct query from available concept descriptors Controlled Vocabulary Searcher Choose appropriate concept descriptors Indexer Metadata-Based Query-Document Matching Query Descriptors Document Descriptors

8. Boolean Search Example Canine AND Fox –Doc 1 Canine AND Political action –Empty Canine OR Political action –Doc 1, Doc 2 The quick brown fox jumped over the lazy dog’s back. Document 1 Document 2 Now is the time for all good men to come to the aid of their party. Volunteerism Political action Fox Canine0 0 1 1 1 1 0 0 Descriptor Doc 1Doc 2 [Canine] [Fox] [Political action] [Volunteerism]

9. Applications When implied concepts must be captured –Political action, volunteerism, … When terminology selection is impractical –Searching foreign language materials When no words are present –Photos w/o captions, videos w/o transcripts, … When user needs are easily anticipated –Weather reports, yellow pages, …

10. Yahoo

11. Text Categorization Goal: fully automatic descriptor assignment Machine learning approach –Assign descriptors manually for a “training set” –Design a learning algorithm find and use patterns Bayesian classifier, neural network, genetic algorithm, … –Present new documents System assigns descriptors like those in training set

12. Supervised Learning f 1 f 2 f 3 f 4 … f N v 1 v 2 v 3 v 4 … v N CvCv w 1 w 2 w 3 w 4 … w N CwCw LearnerClassifier New example x 1 x 2 x 3 x 4 … x N CxCx Labelled training examples CwCw

13. Example: kNN Classifier

14. Machine Assisted Indexing Goal: Automatically suggest descriptors –Better consistency with lower cost Approach rule-based expert system –Design thesaurus by hand in the usual way –Design an expert system to process text String matching, proximity operators, … –Write rules for each thesaurus/collection/language –Try it out and fine tune the rules by hand

15. Machine Assisted Indexing Example //TEXT: science IF (all caps) USE research policy USE community program ENDIF IF (near “Technology” AND with “Development”) USE community development USE development aid ENDIF near: within 250 words with: in the same sentence Access Innovations system:

16. Thesaurus Design Thesaurus must match the document collection –Literary warrant Thesaurus must match the information needs –User-centered indexing Thesaurus can help to guide the searcher –Broader term (“is-a”), narrower term, used for, …

17. Challenges Changing concept inventories –Literary warrant and user needs are hard to predict Accurate concept indexing is expensive –Machines are inaccurate, humans are inconsistent Users and indexers may think differently –Diverse user populations add to the complexity Using thesauri effectively requires training –Meta-knowledge and thesaurus-specific expertise

18. Named Entity Tagging Machine learning techniques can find: –Location –Extent –Type Two types of features are useful –Orthography e.g., Paired or non-initial capitalization –Trigger words e.g., Mr., Professor, said, …

20. Normalization Variant forms of names (“name authority”) –Pseudonyms, partial names, citation styles Acronyms and abbreviations –Organizations, political entities, projects, … Co-reference resolution –References to roles or objects rather than names –Anaphoric pronouns for an antecedent name

21. Example: Bibliographic References

22. Types of Metadata Standards What can we describe? –Dublin Core How can we convey it? –Resource Description Framework (RDF) What can we say? –LCSH, MeSH, … What does it mean? –Semantic Web

23. Dublin Core Goals: –Easily understood, implemented and used –Broadly applicable to many applications Approach: –Intersect several standards (e.g., MARC) –Suggest only “best practices” for element content Implementation: –16 optional and repeatable “elements” Refined using a growing set of “qualifiers” –“Best practice” suggestions for content standards

24. Dublin Core Elements Content Title Subject Description Type Audience Coverage Related resource Rights Instantiation Date Format Language Identifier Responsibility Creator Contributor Source Publisher

25. Resource Description Framework XML schema for describing resources Can integrate multiple metadata standards –Dublin Core, P3P, PICS, vCARD, … Dublin Core provides a XML “namespace” –DC Elements are XML “properties DC Refinements are RDF “subproperties” –Values are XML “content”

26. A Rose By Any Other Name … <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"> <rdf:Description rdf:about="http://media.example.com/audio/guide.ra"> Rose Bush A Guide to Growing Roses Describes process for planting and nurturing different kinds of rose bushes. 2001-01-20

27. Semantic Web RDF provides the schema for interchange Ontologies support automated inference –Similar to thesauri supporting human reasoning Ontology mapping permits distributed creation –This is where the magic happens

28. Adversarial IR Search is user-controlled suppression –Everything is known to the search system –Goal: avoid showing things the user doesn’t want Other stakeholders have different goals –Authors risk little by wasting your time –Marketers hope for serendipitous interest Metadata from trusted sources is more reliable

29. Index Spam Goal: Manipulate rankings of an IR system Multiple strategies: –Create bogus user-assigned metadata –Add invisible text (font in background color, …) –Alter your text to include desired query terms –“Link exchanges” create links to your page

30. Putting It All Together Free TextBehaviorMetadata Topicality Quality Reliability Cost Flexibility

31. Before You Go! On a sheet of paper, please briefly answer the following question (no names): What was the muddiest point in today’s lecture?