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.
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 meeting:
Introduction to the concept of an organizing system and the five facets along which one can analyze organizing systems.
For today you will read about or examine directly six different organizing systems. As you do the readings below (each one is quite short) think about how you would locate each one in the “design space” introduced by Glushko in chapter one of TDO.
To read before this meeting:
We will continue analyzing different kinds of organizing systems by situating them in a five-dimensional design space.
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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.
Note: You only need to read sections 3.1–3.4 of “Models of the Information Seeking Process,” and sections 5.3–5.4 of “Organization Systems.”
To read before this meeting:
Broad overview of the ways organizing schemes and structures are deployed on Web sites.
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.
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An organizing system either explicitly creates, or assumes the existence of, a framework for identifying things.
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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.
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.
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To read before this meeting:
What is the purpose of resource description? What resource properties should be described? How are resource descriptions created? What makes a good resource description?
Ryan will be out of town at the PHOIBOS2 workshop, so Melanie Feinberg will substitute.
To read before this meeting:
To read before this meeting:
How metadata schemas and controlled vocabularies are used to describe, catalogue, and index works of art and architecture, and images of them.
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.
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Defines “relationship” and introduces five perspectives for analyzing relationships among resources: semantic, lexical, structural, architectural, and implementation.
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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 ontopia.net site may be down, so don’t overlook the alternative PDF link above.
A video about the Semantic Web.
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.
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 meeting:
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.
As this examination of citation analysis shows, interpretations can vary widely as to what “links” in a given structure mean.
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.
Total amount of required reading for this meeting: 5,000 words
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 meeting:
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.
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 meeting:
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.
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Read sections 9.1 to 9.3 of Chapter 9, “Interactions with Resources” for today.
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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:
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.
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 schema.org. Skim the schema.org documentation and take a look at the model for structuring recipes.
To read before this meeting:
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:
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).
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.
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 meeting:
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.
Stable standards are dead standards.
For today, read sections 6.5 (“Implementing Categories”) and 7.6 (“Computational Classification”) of TDO, and chapters 3 (“Learning”) and 4 (“Types of Machine Learning”) of A First Encounter with Machine Learning.
To read before this meeting:
Today will be an open session for working on / troubleshooting your “Classifying with Facets” assignment.
The final exam is scheduled for 12PM.