Organization of Information

UNC School of Information and Library Science, INLS 520, Fall 2012

August 21

First, we get to know each other a bit. Then, the basics: how the the class meetings will be run, how you’ll be evaluated, expectations regarding readings and assignments, and so on. Finally, a brief and high-level overview of the topics that will be covered in the course, and how they are related.

August 23
The Organizing System

This course is an introduction to the conceptual foundations of information organization and retrieval: identifying things, describing things, grouping things, relating things, and selecting things. Traditionally these things have been textual documents in the narrow sense: books, periodicals, letters, administrative records, etc.—the kinds of things organized by libraries and archives. But the principles that underlie organization in libraries and archives can be generalized and applied to organize documents and information more broadly, in a variety of contexts. To emphasize what these contexts have in common, rather than how they differ, we will use the abstract notion of an organizing system.

An organizing system is an intentionally arranged collection of resources and the interactions they support. Explicitly or by default, an organizing system makes many interdependent decisions about the identities of things of interest and the ways they are represented as “information.” The organizing system defines how things will be named and described, how they can be grouped and related, and how people or software can create, transform, combine, compare and otherwise use these names, descriptions, groups and relations. When considering the how to make these decisions, we can ask five questions: What is being organized? Why it is being organized? How much is it being organized? When is it being organized? By whom (or by what computational processes) it is being organized?

To read before this class:

  1. Glushko, Robert J. “1. Foundations for Organizing Systems.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    Introduction to the concept of an organizing system and the five facets along which one can analyze organizing systems.

  2. Svenonius, Elaine. “Information Organization.” In The Intellectual Foundation of Information Organization, 1–14. Cambridge, Massachusetts: MIT Press, 2000. PDF.
    Reading tips

    Defines the concepts of information and document and proposes a framework for thinking about what systems for organizing information are. Explains why we need a set of principles for designing these systems.

  3. Morville, Peter, and Louis Rosenfeld. “Organization Systems.” In Information Architecture for the World Wide Web, 53–81. 3rd ed. Sebastopol, California: O’Reilly, 2006.
    Reading tips

    Broad overview of the ways organizing schemes and structures are deployed on Web sites.

August 28
Analyzing Organizing Systems I

To read before this class:

  1. Baron, Richard J., Elizabeth L. Fabens, Melissa Schiffman, and Erica Wolf. “Electronic Health Records: Just Around the Corner? Or over the Cliff?” Annals of Internal Medicine 143, no. 3 (August 2, 2005): 222–226.
  2. McGrath, Sean, and Fergal Murray. Principles of E-Government Architecture. Propylon, July 7, 2003. PDF.
  3. Manzini, Ezio, and Carlo Vezzoli. Product-Service Systems and Sustainability. United Nations Environment Program, 2002.
  4. Miner, Edward A., and Cliff Missen. “‘Internet in a Box’: Augmenting Bandwidth with the eGranary Digital Library.” Africa Today 52, no. 2 (December 1, 2005): 21–37.
  5. Qvenild, Marte. “Svalbard Global Seed Vault: A ‘Noah’s Ark’ for the World’s Seeds.” Development in Practice 18, no. 1 (February 1, 2008): 110–116.
  6. Roy, Hugo, Michiel de Jong, Jan-Christoph Borchardt, and Unhosted. “ToS;DR.” Terms of Service; Didn’t Read, n.d.

August 30
Analyzing Organizing Systems II

We will continue analyzing different kinds of organizing systems by situating them in a five-dimensional design space.

September 4
Activities in Organizing Systems

To read before this class:

  1. Glushko, Robert J., Erik Wilde, Jess Hemerly, Isabelle Sperano, and Robyn Perry. “2. Activities in Organizing Systems.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    When we take an expansive view of organizing systems we can identify four activities that all organizing systems support or perform: selecting resources, organizing resources, designing resource-based interactions, and maintaining resources. These four activities are deeply ingrained in curricula and practice for organizing systems like libraries and museums, and they can be extended to other kinds of organizing systems employed by individuals, groups and enterprises in various domains.

  2. Wright, Alex. “Managing Scientific Inquiry in a Laboratory the Size of the Web.” The New York Times, December 27, 2010, sec. Science.

September 6
Resources in Organizing Systems I

An organizing system reflects (or produces or enforces) a specific view of the world by defining what the resources being organized are. This involves making decisions about when things are to be considered the same or different, i.e. how they are to be identified. Decisions about identity and identification define the basic units of organization, and these decisions have consequences for every other aspect of the organizing system.

To read before this class:

  1. Glushko, Robert J., Daniel D. Turner, Kimra McPherson, and Jess Hemerly. “3. Resources in Organizing Systems.” In The Discipline of Organizing, edited by Robert J Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    An organizing system either explicitly creates, or assumes the existence of, a framework for identifying things.

  2. Coyle, Karen. “Identifiers: Unique, Persistent, Global.” The Journal of Academic Librarianship 32, no. 4 (2006): 428–431.
  3. Berners-Lee, Tim. Cool URIs Don’t Change. W3C Style. W3C, 1998.
  4. The Echo Nest. “Announcing Echoprint.” The Echo Nest Blog, June 23, 2011.

September 11
Ryan at HTRC UnCamp

We will not meet today as I will be attending the HTRC UnCamp.

Please do the readings below, and spend some time familiarizing yourself with XML. In addition to the readings you may find the XML tutorial at W3Schools helpful.

You may already have some familiarity with XML, but perhaps mostly as a data format for applications or programming. In IO and IR it is essential to take a more abstract and intellectual view of XML and understand how it represents structured information models. XML encourages the separation of content from presentation, which is an important principle of information architecture. Encoding information in XML is an investment in information organization that pays off “downstream” in IR and language processing applications.

To read before this class:

  1. Birnbaum, David J. “What is XML and why should humanists care? An even gentler introduction to XML”, January 5, 2012.
  2. Glushko, Robert J. “XML Foundations.” In Document Engineering, 42-72. Cambridge, Massachusetts: MIT Press, 2005.

September 13
Resources in Organizing Systems II

To read before this class:

  1. Kent, William. “Entities.” In Data and Reality, v–19. Amsterdam: North-Holland, 1978. PDF.
    Reading tips

    Through its (explicit or implicit) framework of identity and identification, an organizing system defines a set of entities. These entities are a model, not of reality, but of how some people or organizations process information about reality.

  2. Brisbane, Arthur S. “On, Now You See It, Now You Don’t.” The New York Times, June 25, 2011, sec. Opinion / Sunday Review.
  3. Smith, Abby. “Authenticity in Perspective.” In Authenticity in a Digital Environment. Council on Library and Information Resources, 2000.

September 18
Resource Description and Metadata I

Scoping & Identifying Resources due

To read before this class:

  1. Glushko, Robert J., Kimra McPherson, Ryan Greenberg, Matthew Mayernik, Graham Freeman, and Carl Lagoze. “4. Resource Description and Metadata.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    What is the purpose of resource description? What resource properties should be described? How are resource descriptions created? What makes a good resource description?

  2. Wheatly, Malcolm. “Operation Clean Data.” CIO, September 10, 2005.
  3. Whitman, Brian. “Why Music ID Resolution Matters to Every Music Fan on Facebook.”, 2011.

September 20
Resource Description and Metadata II

To read before this class:

  1. Kent, William. “The Nature of an Information System.” In Data and Reality, 21–40. Amsterdam: North-Holland, 1978. PDF.
  2. Kent, William. “Naming.” In Data and Reality, 41–61. Amsterdam: North-Holland, 1978. PDF.

September 25
Describing Relationships and Structures I

Organizing systems do not simply describe and enable interactions with resources in isolation: they provide frameworks for relating resources to one another in useful ways.

To read before this class:

  1. Glushko, Robert J., Matthew Mayernik, Alberto Pepe, and Murray Maloney. “5. Describing Relationships and Structures.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    Defines “relationship” and introduces five perspectives for analyzing relationships among resources: semantic, lexical, structural, architectural, and implementation.

  2. Kent, William. “Relationships.” In Data and Reality, 63–76. Amsterdam: North-Holland, 1978. PDF.
  3. Fellbaum, Christiane. “WordNet.” In Theory and Applications of Ontology: Computer Applications, edited by Roberto Poli, Michael Healy, and Achilles Kameas, 231–243. Springer Netherlands, 2010.

September 27
Describing Relationships and Structures II

Creating a Vocabulary & Descriptions due

To read before this class:

  1. Pepper, Steve. The TAO of Topic Maps: Finding the Way in the Age of Infoglut, 2000. PDF.
    Reading tips

    Topic maps are an ISO standard for describing knowledge structures and associating them with information resources. Topic maps are grounded in a basic model consisting of Topics, Associations, and Occurrences (TAO).

    The site may be down, so don’t overlook the alternative PDF link above.

  2. Ray, Kate. Web 3.0, 2010.
    Reading tips

    A video about the Semantic Web.

  3. Heath, Tom, and Christian Bizer. “Introduction.” In Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool, 2011.
  4. Weinberger, David. The Molecule of Data. Mp3. Library Lab, n.d.
    Reading tips

    In this podcast Karen Coyle explains why libraries are keen on the idea of using Linked Data to produce more value from their cataloging efforts.

October 2
Exploiting Relationships and Structures

Structure-based IR models combine representations of terms with information about structures within documents (i.e., hierarchical organization) and between documents (i.e. hypertext links and other explicit relationships). This structural information tells us what documents and parts of documents are most important and relevant, and provides additional justification for determining relevance and ordering a result set. The nature and pattern of links between documents has been studied for almost a century by “bibliometricians” who measured patterns of scientific citation to quantify the influence of specific documents or authors. The concepts and techniques of citation analysis seem applicable to the web since we can view it as a network of interlinked articles, and Google’s “page rank” algorithm is now the classic example. With the advent of “social media” there are now a wealth of new potential sources of structural metadata.

To read before this class:

  1. Diaz, Alejandro M. “Through the Google Goggles: Sociopolitical Bias in Search Engine Design”. Stanford University, 2005.
    Reading tips

    The most famous and influential exploitation of “structural metadata” is PageRank, the secret sauce behind Google search (and now all other major search engines). While the idea behind PageRank is simple, its implications as a system for mediating access to information are not. Read only chapters 4 and 5.

  2. MacRoberts, M. H, and Barbara R MacRoberts. “Problems of Citation Analysis.” Scientometrics 36, no. 3 (July 1996): 435–444.
    Reading tips

    As this examination of citation analysis shows, interpretations can vary widely as to what “links” in a given structure mean.

  3. Mislove, Alan, Krishna P. Gummadi, and Peter Druschel. “Exploiting Social Networks for Internet Search.” In Record of the Fifth Workshop on Hot Topics in Networks: HotNets V. Irvine, CA: ACM SIGCOMM, 2006.

October 4
Categories: Describing Resource Classes and Types

We impose meaning on the world by “carving it up” into concepts and categories. The conceptual and category boundaries we impose treat some things or instances as equivalent and others as different. Sometimes we do this implicitly and sometimes we do it explicitly. We do this as members of a culture and language community, as individuals, and as members of organizations or institutions. Across these different contexts the mechanisms and outcomes of our categorization efforts differ. In most cases the resulting categories are messier than our information systems and applications can handle, and understanding why and what to do about it are essential skills for information professionals.

To read before this class:

  1. Glushko, Robert J., Rachelle Annechino, Jess Hemerly, and Longhao Wang. “6. Categorization: Describing Resource Classes and Types.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.
    Reading tips

    What categories are, how they are used in information management, and how changes in the understanding of human cognitive processes have altered theories of categorization over the years.

  2. Glushko, Robert J, Paul P Maglio, Teenie Matlock, and Lawrence W Barsalou. “Categorization in the Wild.” Trends in Cognitive Sciences 12, no. 4 (April 2008): 129–35.
    Reading tips

    In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important.

October 9
Classification I: Assigning Resources to Categories

A classification is a system of categories, ordered according to a pre-determined set of principles and used to organize a set of instances or entities. This doesn’t mean that the principles are always good or equitable or robust: every classification is biased in one way or another. Classifications are embodied in every information-intensive activity or application.

To read before this class:

  1. Glushko, Robert J., Jess Hemerly, Vivien Petras, Michael Manoochehri, Longhao Wang, Jordan Shedlock, and Daniel Griffin. “7. Classification: Assigning Resources to Categories.” In The Discipline of Organizing, 3rd ed. O’Reilly, 2015.
    Reading tips

    The terms “classification” and “categorization””are often used interchangeably, but they are not the same. Having a set of categories is not sufficient to create a classification. A classification must be principled so that we know where to place new items and entities in accordance with our system.

  2. Kent, William. “Attributes.” In Data and Reality, 77–84. Amsterdam: North-Holland, 1978. PDF.
  3. Kent, William. “Types and Categories and Sets.” In Data and Reality, 85–91. Amsterdam: North-Holland, 1978. PDF.

October 11
Classification II: Classification Structures

Building a Taxonomy due

To read before this class:

  1. Lambe, Patrick. “Taxonomies can take many forms.” In Organising Knowledge: Taxonomies, Knowledge and Organisational Effectiveness, 4-48. Oxford: Chandos, 2007. PDF.
  2. Hearst, Marti. “UIs for Faceted Navigation: Recent Advances and Remaining Open Problems.” In Proceedings of the Workshop on Computer Interaction and Information Retrieval (HCIR 2008). Redmond, Washington, 2008.
  3. Golder, Scott, and Bernardo A. Huberman. “The Structure of Collaborative Tagging Systems.” arXiv:cs/0508082 (August 18, 2005).

October 16
Classification III: Automatic Classification

Special guest: Jane Greenberg will come and tell us about her HIVE project.

Increasingly, classification of resources is done by algorithms. Knowing what algorithms can and can’t do is critical for understanding the potential of automatic classification.

To read before this class:

  1. Hedden, Heather. “Taxonomies for Automated Indexing.” In The Accidental Taxonomist, 199–230. Medford, New Jersey: Information Today, 2010. PDF.
    Reading tips

    Automated indexing is a very broad notion that encompasses various technologies and techniques, some of which involve taxonomies and some of which do not. Automated tagging, auto-classification, and auto-categorization refer to automated indexing technologies that utilize taxonomies in some way or another. Simpler search engines perform a form of automated indexing without using taxonomies, but more recently, some search systems have incorporated taxonomies.

  2. Greenberg, Jane, Robert Losee, José Ramón Pérez Agüera, Ryan Scherle, Hollie White, and Craig Willis. “HIVE: Helping Interdisciplinary Vocabulary Engineering.” Bulletin of the American Society for Information Science and Technology 37, no. 4 (2011): 23–26.
  3. Graham, Paul. “A Plan for Spam”, August 2002.
  4. Li, Jiexun, Rong Zheng, and Hsinchun Chen. “From Fingerprint to Writeprint.” Commun. ACM 49, no. 4 (April 2006): 76–82.

October 18
Fall break

No class.

October 23
Standards for Organizing I

Until now we’ve focused on developing a conceptual understanding of how to define and describe entities and types of entities when organizing information. However to progress further we must familiarize ourselves with some of the various (and constantly evolving) methods and standards for formally expressing these concepts in machine-readable ways, and for guiding information organization processes to ensure consistency and interoperability. Today we’ll look at two kinds of standards: standardized syntaxes for data interchange and standardized conceptual or structural models.

In addition to the required reading below (“The Forms of Resource Descriptions”), here are some additional resources you may find useful:

Standard syntaxes for data interchange

Syntax governs the arrangement of symbols to create properly formed (but not necessarily meaningful) messages.

The dominant syntax standard for encoding data so that it can be exchanged among different organization systems is the eXtensible Markup Language (XML). Review the XML Foundations reading from 8/31, and the XML tutorials at ZVON and W3Schools if you’ve forgotten what you learned about XML.

An increasingly popular alternative syntax standard is JavaScript Object Notation (JSON). Read JSON: The Fat-Free Alternative to XML.

Standard conceptual or structural models

Conceptual or structural models aim to standardize the way information is conceptualized. They can range from very abstract to very specific. Unlike syntax standards, they do not specify how symbols are arranged but instead specify basic concepts and how they are related to one another. However, conceptual or structural models often specify how their concepts should be represented in one or more syntaxes.

As we discussed in class two weeks ago, The Resource Description Framework (RDF) is the conceptual model at the foundation of the Semantic Web. It is a very abstract conceptual model because it aims to standardize concepts suitable for modeling any kind of data. Watch Jenn Riley’s RDF for Librarians presentation for a more detailed explanation of RDF.

A higher-level yet still rather abstract conceptual model is the Functional Requirements for Bibliographic Records (FRBR). Read What is FRBR?

The Atom Syndication Format is a model for describing the structure of blog feeds, or any kind of data that can be expressed as a list of time-stamped items. Atom is an example of a structural model that is relatively tightly tied to a specific syntax (XML).

Google recently released the Dataset Publishing Language (DSPL), a new conceptual model for describing quantitative datasets such as demographic statistics. Skim through the DSPL Tutorial.

Finally there are conceptual or structural models for relatively concrete, well-understood kinds of things such as contact information, calendar events, postal addresses, and recipes. Recently the three major search engines agreed on a set of conceptual models for these types of information and published them at Skim the documentation and take a look at the model for structuring recipes.

To read before this class:

  1. Shaw, Ryan, and Murray Maloney. “8. The Forms of Resource Descriptions.” In The Discipline of Organizing, edited by Robert J. Glushko, 3rd ed. O’Reilly, 2015.

October 25
Standards for Organizing II

Today we’ll look at two more kinds of standards: standardized values or names and standardized processes. We’ll wrap up by considering how technical standards and transformation techniques can help achieve integration and interoperability, acknowledging that interoperability is not always possible and that non-technical factors play a huge role in determining the approach. In addition to the required reading below (“Why Standardization Efforts Fail”), here are some additional resources you may find useful:

Standard values or names: Controlled vocabularies & thesauri

Conceptual or structural models usually define the kinds of attributes that entities have, but may not specify the actual values that those attributes can take. This is the role of value standards, which are usually lists or hierarchies of names or identifiers that can be used as values for certain kinds of attributes.

A very simple example of a value standard is ISO 3166-1, which standardizes 2 and 3-letter codes for identifying countries.

More complex value standards resemble (or are) classifications, with faceted and/or hierarchical structure. Browse through the Art & Architecture Thesaurus, the AGROVOC agricultural vocabulary, and the Medical Subject Headings (MeSH).

Standard processes: Rules & best practices

Finally, rules or best practices seek to standardize the processes by which people organize information. Among other things, they may specify when and how the other kinds of standards should be used to describe and organize particular kinds of information.

Although not an official standard, the database guidelines at Discogs are a good example of what rules for cataloging look like. Read the Quick Start Guide and skim through some of the other database guidelines such as Genres/Styles and Master Release.

An example of a more official standard is Graphic Materials: Rules for Describing Original Items and Historical Collections, which provides rules for describing photographs, posters, cartoons, prints and drawings. Skim through the standard to get a sense of the variety of aspects of the description process that it attempts to standardize.

October 30
Ryan at ASIS&T Annual Meeting

No class.

November 1
Standards Development & Governance

Classifying with Facets due

Today we’ll consider the vocabulary problem as it manifests itself across organizational contexts. Within an organization, different information systems might use data models that are incomplete or incompatible with respect to each other, and between organizations these differences can be even greater. Structural, syntactic, and semantic mismatches cause problems when processes and services attempt to span these system and organizational boundaries (for example, to create a complete model of a “customer” or to conduct a business transaction). We’ll consider how technical standards and transformation techniques can help achieve integration and interoperability, but we’ll acknowledge that interoperability is not always possible and that non-technical factors play a huge role in determining the approach.

To read before this class:

  1. Cargill, Carl F. “Why Standardization Efforts Fail.” Journal of Electronic Publishing 14 (2011).
    Reading tips

    The ostensible failure of a standard has to be examined not so much from the focus of whether the standard or specification was written or even implemented (the usual metric), but rather from the viewpoint of whether the participants achieved their goals from their participation in the standardization process.

  2. Mazzocchi, Stefano. “Interoperability by Friction.” Stefano’s Linotype, 2008.
    Reading tips

    Stable standards are dead standards.

November 6
Midterm Review

Be sure to review the list of terms and concepts you should know for the midterm.

November 8

Midterm Exam due

The midterm will be given during regular class time. It will be distributed as a Word document, so you’ll need to bring a laptop to work on it. It is open-book, open-notes.

November 13
Trunk Wrap-up / Branches Kick-off

This will be the last day that we meet as a class until November 27. Until then you will work on your own and with your branch groups, checking in with me periodically as needed.

November 27
Cataloging Branch Presentation

November 29
Database Branch Presentation

December 4
Archives Branch Presentation

December 13
Branch Deliverables Due

Branch Deliverables due

Submit your branch deliverables by 3PM today.