1. SKOS-2-HIVE GWU workshop

2. Introductions Hollie White hcwhite1@email.unc.edu Jane Greenberg janeg@email.unc.edu

3. Morning Session Schedule Introductions Section 1: Characterizing Knowledge Organization Structures Section 2: Thesauri and What They Represent BREAK Section 3: From Thesauri to SKOS Section 4: From SKOS to HIVE Exploring HIVE

4. Section 1: Characterizing knowledge organization structures

5. Types of knowledge organization structures From least to most structure Term lists Controlled vocabularies Thesauri Taxonomy Ontology

6. Languages for aboutness Indexing languages: Terminological tools Thesauri (CV – controlled vocabulary) Subject headings lists Authority files for named entities (people, places, structures, organizations) Classification / Classificatory systems Keyword lists Natural language systems (broad interpretation) 6

7. Term lists Controlled but semi-unstructured list Term List in practice http://library.lib.asu.edu/search/y

8. Authority files -standardization of names, subjects and titles for easier identification and interoperability of information Authority Files: http://authorities.loc.gov/

9. Thesauri Less-structured and structured thesauri Lexical semantic relationships Composed of indexing terms/descriptors Descriptors - representations of concepts Concepts - Units of meaning

10. Thesaurus basics Preferred terms vs. non-preferred terms --ex. dress vs. clothing Semantic relations between terms --broader, narrower, related How to apply terms (guidelines, rules) Scope notes

11. Common thesaural identifiers SN Scope Note Instruction, e.g. don’t invert phrases USE Use (another term in preference to this one) UF Used For BT Broader Term NT Narrower Term RT Related Term

12. Controlled Vocabularies (less structured thesauri also referred to as subject heading lists) Library of Congress Subject Headings (LCSH) Sears Subject Headings Medical Subject Headings (MeSH) http://www.nlm.nih.gov/mesh/MBrowser.html

13. Thesauri Thesaurus in practice ERIC NBII http://thesaurus.nbii.gov/portal/server.pt NASA thesaurus http://www.sti.nasa.gov/thesfrm1.htm

14. Taxonomy First used by Carl von Linne (Linneaus) to classify zoology. A grouping of terms representing topics or subject categories. A taxonomy is typically structured so that its terms exhibit hierarchical relationships to one another, between broader and narrower concepts. taxonomy == a subject-based classification that arranges the terms in the controlled vocabulary into a hierarchy (Garshol 2004)

15. Ontology In general (in the LIS domain): a tool to help organize knowledge a way to convey or represent a class (or classes) of things, and relationships among the class/es. No exact definition…this comes from the community you are coming from 15

16. KOS used in Digital Libraries Looked at 269 online digital libraries and collections KOS used: Locally developed taxonomy (113) LCSH (78) Author list (34) Thesauri (26) Alphabetical listing (20) Geographic arrangement (16) Shiri, A. and Chase-Kruszewski, S. (2009) Knowledge organization systems in North American digital library collections. Program:electronic library and information systems. 43 (2) pp 121-139.

17. Discussion: Think about your own organization. What type of controlled vocabularies, thesauri, and ontologies does your organization use for everyday work? How do these vocabulary choices help you meet the goals of your institution?

18. Organizing Knowledge Organization Structures

19. Hodge’s Types of Knowledge Organization Systems Terms Lists : Authority Files, Glossaries, Gazetteers, Dictionaries Classifications and Categories: Subject Headings, Classification Schemes, Taxonomies, and Categorization Schemes Relationship Lists: Thesauri, Semantic Networks, Ontologies Hodge, G. (2000) Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files.http://www.clir.org/pubs/abstract/pub91abst.htmlhttp://www.clir.org/pubs/abstract/pub91abst.html

20. (McGuinness, D. L. (2003). Ontologies Come of Age. In Fensel, et al, Spinning the Semantic Web. Cambridge, MIT Press), pp. 175. [see also, p. 181 + 189])

21. Classical view of ILS languages Simple thesauri/ deeper taxonomies low level full/intricate Key word CV thesauri ontologies ontologies Lists (WordNet) (OWL) Greenberg’s Ontology Continuum

22. (http://jodi.tamu.edu/Articles/v04/i04/Smith/#section12)http://jodi.tamu.edu/Articles/v04/i04/Smith/#section12

23. http://www.semantic-conference.com

24. Section 2: Thesauri and what they represent

25. Examples of different types of “thesauri” Cook’s Thesaurus http://www.foodsubs.com/ BZZURKK! Thesaurus of Champions http://epe.lac- bac.gc.ca/100/200/300/ktaylor/kaboom/bzzurkk.htm General Multilingual Environmental Thesaurus http://www.eionet.europa.eu/gemet

26. Common thesaural identifiers SN Scope Note Instruction, e.g. don’t invert phrases USE Use (another term in preference to this one) UF Used For BT Broader Term NT Narrower Term RT Related Term

27. Syndetic Relationships Hierarchical Equivalent Associative

28. Hierarchical Level of generality – both preferred terms BT (broader term) Birthday cakes BT Cakes NT (narrower term) Cakes NT Birthday cakes …remember inheritance

29. Equivalent When two or more terms represent the same concept One is the preferred term ( descriptor ), where all the information is collected The other is the non-preferred and helps the user to find the appropriate term

30. Equivalent Non-preferred term USE Preferred term – Biological diversification USE Biodiversity Preferred term UF (used for) Non- preferred term – Biodiversity UF Biological diversification

31. Associative One preferred term is related to another preferred term Non-hierarchical “See also” function In any large thesaurus, a significant number of terms will mean similar things or cover related areas, without necessarily being synonyms or fitting into a defined hierarchy

32. Associative Related Terms ( RT ) can be used to show these links within the thesaurus – Bed RT Bedding – Paint Brushes RT Painting – Vandalism RT Hostility – Programming RT Software

33. Exercise: Thesauri Building Montages Digital photographs Illustrations Pictures Photographic prints Drawings Photographs Daguerreotypes Negatives

34. Where to start: Look at the overall offering Determine the aboutness Identify the “root” element or broadest term Identify groups/categories of information Start structuring based on the syndetic relations you know Create hierarchies based on the semantic relations Use the appropriate identifiers to show the relationships

35. Section 3: From Thesauri to SKOS

36. Simple Knowledge Organization Systems Classical view of ILS languages Simple thesauri/ deeper taxonomies low level full/intricate Key word CV thesauri ontologies ontologies Lists (i.e WordNet) (i.e. OWL) SKOS

37. Example 1:web view of NBII entry

39. Descriptive Markup “the markup is used to label parts of the document rather than to provide specific instructions as to how they should be processed. The objective is to decouple the inherent structure of the document from any particular treatment or rendition of it. Such markup is often described as "semantic". --from Wikipedia

40. Markup Languages “is a system for annotating a text in a way which is syntactically distinguishable from that text.”annotating Using tags: content to be rendered Or a keyword in brackets to distinguish texts --from Wikipedia

41. HTML Hypertext Markup Language --language used to mark up webpages --both descriptive and processing

42. HTML encoding Hello HTML Hello World!

43. NBII in HTML Heterozygotes BT Genotypes NT Carriers (genetics) RT Heterozygosity RT Homozygotes SC LSC LifeSciences Homozygotes BT Genotypes RT Heterozygotes RT Homozygosity SC LSC LifeSciences ;

44. XML Extensible Markup Language --Created by the World Wide Web Consortium (W3C). --Used to mark up documents on the internet or electronic documents. --Users get to describe the tags that are used and define how they are used.

45. XML encoding

46. NBII in XML Zygotes Ookinetes Ova Oocysts Hemizygosity Reproduction Zygosity ASF Aquatic Sciences and Fisheries LSC Life Sciences Approved Descriptor 2007-08-14

47. RDF Resource Description Framework “is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax formats”World Wide Web Consortiumspecificationsmetadatadata model --from Wikipedia

48. RDF data model is similar to Entity-Relationship or Class diagrams,Entity-RelationshipClass diagrams statements about resource in subject-predicate- object expressions called “triples”.statements subject = resource predicate = traits or aspects of the resource and expresses a relationship between the subject and the object.

49. The sky has the color blue RDF triple: a subject denoting "the sky“ a predicate denoting "has the color” an object denoting "blue”

50. OWL Web Ontology Language --knowledge representation language for displaying ontologies working with logic

51. SKOS Family of languages used to describe thesauri, controlled vocabulary, subject headings, and taxonomies.

52. NBII in SKOS/RDF Ookinetes Zygotes ASF Aquatic Sciences and Fisheries LSC LifeSciences

53. Basic SKOS Tags Skos:concept Skos:prefLabel Skos:altLabel Skos:broader Skos:narrower Skos:related

54. SKOS tags SN Scope Note = skos:scopeNote USE Use = skos:prefLabel UF Used For =skos:altLabel BT Broader Term = skos:broader NT Narrower Term = skos:narrower RT Related Term = skos:related Each entry term has a skos:concept

55. Terms vs. Concepts? Example: Table Lexical level : Table Conceptual level :

56. What is a SKOS Concept? Zygotes BT Ova NTOocysts RTHemizygosity RTReproduction RTZygosity UFOokinetes All these relationships make up a SKOS concept

57. Projects Using SKOS: Library of Congress http://id.loc.gov/authorities/search/ Europeana http://www.europeana.eu/portal/ HIVE http://ils.unc.edu/mrc/hive/

58. EXPERIMENTING WITH SKOS Instructions: SKOS tags can easily be mapped to identifiers found in traditional thesauri. For this activity try mapping basic SKOS tags to an TGM: Subject Terms excerpt.

59. Section 4: From SKOS to HIVE

60. Overview HIVE—Helping Interdisciplinary Vocabulary Engineering Motivation—Dryad repository HIVE—Goals, status, and design A scenario Usability Conclusion and questions

61. 61 HIVE model  approach for integrating discipline CVs  Model addressing C V cost, interoperability, and usability constraints (interdisciplinary environment)

62. Motivation

63. 63

64. American Society of Naturalists American Naturalist Ecological Society of America Ecology, Ecological Letters, Ecological Monographs, etc. European Society for Evolutionary Biology Journal of Evolutionary Biology Society for Integrative and Comparative Biology Integrative and Comparative Biology Society for Molecular Biology and Evolution Molecular Biology and Evolution Society for the Study of Evolution Evolution Society for Systematic Biology Systematic Biology Commercial journals Molecular Ecology Molecular Phylogenetics and Evolution Partner Journals

65. Dryad’s workflow ~ low burden submission

66. Vocabulary needs for Dryad Vocabulary analysis – 600 keywords, Dryad partner journals Vocabularies: NBII Thesaurus, LCSH, the Getty’s TGN, ERIC Thesaurus, Gene Ontology, IT IS (10 vocabularies) Facets: taxon, geographic name, time period, topic, research method, genotype, phenotype… Results 431 topical terms, exact matches – NBII Thesaurus, 25%; MeSH, 18% 531 terms (research method and taxon) – LCSH, 22% found exact matches, 25% partial Conclusion: Need multiple vocabularies

67. Goals, status, and design

68. HIVE... as a solution Address CV (controlled vocabulary) cost, interoperability, and usability constraints COST: Expensive to create, maintain, and use INTEROPERABILITY: Developed in silos (structurally and intellectually) USABILITY: Interface design and functionality limitations have been well documented

69. HIVE Goals − Automatic metadata generation approach that dynamically integrates discipline-specific controlled vocabularies encoded with the Simple Knowledge Organisation System (SKOS)Simple Knowledge Organisation System (SKOS) Provide efficient, affordable, interoperable, and user friendly access to multiple vocabularies during metadata creation activities A model that can be replicated —> model and service Three phases of HIVE: 1. Building HIVE - Vocabulary preparation - Server development - Primate Life Histories Working Group - Wood Anatomy and Wood Density Working Group 2. Sharing HIVE empowering information professionals - Continuing education (empowering information professionals) 3. Evaluating HIVE - Examining HIVE in Dryad

70. HIVE Partners Vocabulary Partners Library of Congress: LCSH the Getty Research Institute (GRI): TGN (Thesaurus of Geographic Names ) United States Geological Survey (USGS): NBII Thesaurus, Integrated Taxonomic Information System (ITIS) Agrovoc Thesaurus Advisory Board Jim Balhoff, NESCent Libby Dechman, LCSH Mike Frame, USGS Alistair Miles, Oxford, UK William Moen, University of North Texas Eva Méndez Rodríguez, University Carlos III of Madrid Joseph Shubitowski, Getty Research Institute Ed Summers, LCSH Barbara Tillett, Library of Congress Kathy Wisser, Simmons Lisa Zolly, USGS WORKSHOPS HOSTS: Columbia Univ.; Univ. of California, San Diego; Univ. of North Texas; Universidad Carlos III de Madrid, Madrid, Spain

71. HIVE Construction HIVE stores millions of concepts from different vocabularies, and makes them available on the Web by a simple HTTP – Vocabularies are imported into HIVE using SKOS/RDF format HIVE is divided in two different modules: 1. HIVE Core – SKOS/RDF storage and management (SESAME/Elmo) – SMART HIVE – SMART HIVE : Automatic Metadata Extraction and Topic Detection (KEA++) – Concept Retrieval (Lucene) 2. HIVE Web – Web user Interface (GWT—Google Web Toolkit) – Machine oriented interface (SOAP and REST)

76. A scenario HIVE for scientists, depositors HIVE for information professionals: curators, professional librarians, archivists, museum catalogers

77. Meet Amy Amy Zanne is a botanist. Like every good scientist, she publishes.

79. ~~~~Amy Amy Zanne is a botanist. Like every good scientist, she publishes. She deposits data in Dryad.

80. Dryad’s workflow ~ low burden submission

85. Usability LS and IS students (32 students) - Understanding HIVE: 3.8 on 5 pt. scale - Ease of navigation: 4.5 - Concept cloud a good idea: 3.3 - Represent document accurately: 2.0 (simple HIVE), 3.3 ( smart HIVE) Advisory board (10 members) - Systems/technical folks want integration w/systems, Getty—EAD - Librarians/KO folks, want to see term relationships - Like tag cloud, want relevance percentages - Color, placement of box, labels.. White 2009-2010; HIVE Team 2009-2010

86. Usability Formal usability study 4 biologist, 5 information professionals ~ Tasks, usability ratings, satisfaction ranking Average time to search a concept: Librarians: 6.53 minutes Scientists: 3.82 minutes ~ consistent w/research at NIEHS, 2 times as long Average time for automatic indexing sequence Librarians: 1.91 minutes Scientists: 2.1 minutes Huang, 2010

87. System usability and flow metrics Huang, 2010

88. Challenges Building vs. doing/analysis Source for HIVE generation, beyond abstracts Combining many vocabularies during the indexing/term matching phase is difficult, time consuming, inefficient. NLP and machine learning offer promise Interoperability = dumbing down ontologies Proof-of-concept/ illustrate the differences between HIVE and other vocabulary registries (NCBO and OBO Foundry) General large team logistics, and having people from multiple disciplines (also the ++)

89. Summary and next steps Open source, customizable, SKOS, + hybrid metadata generation Research and evaluation Team project relating to Dryad Hollie White--dissertation Lesley Skalla--master’s paper Craig Willis– MeSH/SKOS conversion Curator interface design Workshop evaluation User’s and developer’s groups on “Google Groups” Long Term Ecological Research (LTER) Network (http://www.lternet.edu/)http://www.lternet.edu/

90. Exploring HIVE http://hive.nescent.org

91. Questions /Comments Hollie White hcwhite1@email.unc.edu Ryan Scherle ryan@scherle.org Jane Greenberg janeg@email.unc.edu Craig Willis willisca@email.unc.edu