# Presentation: 273881

## 6. Applying metadata standards: Controlled vocabularies and quality issues

- Metadata Standards and Applications Workshop

## Goals of Session

- Understand how different controlled vocabularies are used in metadata
- Investigate metadata quality issues

## Applying metadata:Controlled vocabularies

- Values that occur in metadata
- Often documented and published
- Goal to reduce ambiguity
- Control of synonyms
- Establishment of formal relationships among terms (where appropriate)
- Testing and validation of terms
- Metadata registries may be established

## Why bother?

- To improve retrieval, i.e., to get an optimum balance of _precision_ and _recall_
- _Precision_ – How many of the retrieved records are relevant?
- _Recall_ – How many of the relevant records did you retrieve?

**Notes:**
Research in library science shows that precision and recall ARE important to people
Precision:  how much is noise and garbage?

## Types of Controlled Vocabularies

- Lists of enumerated values
- Taxonomy
- Thesaurus
- Classification Schemes
- Ontology

**Notes:**
NISO (free download – in bibliography)
ANSI/NISO Z39.19-2005:  Guidelines for the Construction, Format, and Management of Monolingual Controlled Vocabularies

## Lists

- A _list_ is a simple group of terms
*    **Example:*
- Alabama
- Alaska
- Arkansas
- California
- Colorado
**. . . .**
-    Frequently used in Web site pick lists and pull down menus

**Notes:**
With these kind of lists, the user is assumed to know what the values mean, or close enough.

## Taxonomies

- A *taxonomy *is a set of preferred terms, all connected by a hierarchy or polyhierarchy
*   **Example:*
*Chemistry*
*	**Organic chemistry*
*	**	**Polymer chemistry*
*	**	**	**Nylon*
- Frequently used in web navigation systems

**Notes:**
“Nylon” could appear in other hierarchies as well, for example taxonomies of biological terms

## Thesauri

- A *thesaurus* is a controlled vocabulary with multiple types of relationships
-    *Example:*
- Rice
-     UF paddy
- BT Cereals
- BT Plant products
- NT Brown rice
- RT Rice straw

**Notes:**
LCSH has “thesaural” pieces in it, although it is not a true thesaurus

## Ontology

- One definition: “An arrangement of concepts and relations based on an underlying model of reality.”
- Ex.: Organs, symptoms, and diseases in medicine
- No real agreement on definition—every community uses the term in a slightly different way

**Notes:**
Ontology:  this word is bandied around a lot.  Most people use the term without knowing what it means.  
According to this definition, it is not intended to be used by a lot of people with different versions of reality, just one version of reality.
At some point, the definition of ontology will settle down.

## Thesaurus Relationships

- Relationship types:
- Use/Used For – indicates preferred term
- Hierarchy – indicates broader and narrower terms
- Associative – almost unlimited types of relationships may be used
-    It is the most complex format for controlled vocabularies and widely used.

**Notes:**
Thesauri are the most useful, and there are good developments going on, in terms of coding.
A hierarchy lets you build many different levels.
Associative relationships can be expressed in different ways.

## Slide 11

**Notes:**
Note that Z39.19 is the content standard, having roughly the same relationship to encoding systems that AACR2 has to MARC 21.

## Equivalence Relationships

- Term A and Term B overlap completely

- A = B

## Hierarchical Relationships

- Term A is included in Term B

- B

- A

**Notes:**
One is broader; the other is narrower.

## Associative Relationships

- Semantics of terms A and B overlap

- A

- B

**Notes:**
Semantics are the meaning– that is what overlays.

## Expressing Relationship

| Relationship | Rel. Indicator | Abbreviation |
| --- | --- | --- |
| Equivalence
  (synonymy) | Use
Used for | None or U
UF |
| Hierarchy | Broader term
Narrower term | BT
NT |
| Association | Related term | RT |

## Vocabulary Management

- The degree of control over a vocabulary is (mostly) independent of its type
**Uncontrolled **– Anybody can add anything at any time and no effort is made to keep things consistent
**Managed **– Software makes sure there is a list that is consistent (no duplicates, no orphan nodes) at any one time. Almost anybody can add anything, subject to consistency rules
**Controlled **– A documented process is followed for the update of the vocabulary. Few people have authority to change the list. Software may help, but emphasis is on human processes and custodianship

**Notes:**
Formal expression of vocabularies
Not folksonomies, not tagging
If you control a vocabulary, the degree of control depends on what kind of vocabulary it is.
A controlled vocabulary is based on a documented process.

## Encoding controlled vocabularies

- MARC 21
- Authority Format used for names, subjects, series
- Classification Format used for formal classification schemes
- MADS (a derivative of MARC)
- Simple Knowledge Organization System (SKOS)
- Intended primarily for concept schemes (e.g., not names)

**Notes:**
In trying to think about this, or one piece of it, we already encode vocabularies for machine manipulation and other activities.
SKOS (rhymes with “floss”) is used for topical kinds of concepts.

## SKOS

- “SKOS Core provides a model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', other types of controlled vocabulary, and also concept schemes embedded in glossaries and terminologies.”
*--SKOS Core Guide*

**Notes:**
Documentation for SKOS is in the bibliography.  SKOS is intended to cover everything and more than what is included in the NISO standard

## The skos:Concept class allows you to assert that a resource is a conceptual resource. That is, the resource is itself a concept. 

**Notes:**
A SKOS conceptual resource is not something you can hold in your hand.

“The SKOS Core Vocabulary is an application of the Resource Description Framework (RDF), that can be used to express a concept scheme as an RDF graph. Using RDF allows data to be linked to and/or merged with other data, enabling data sources to be distributed across the web, but still be meaningfully composed and integrated.”
--SKOS Core Guide

## Preferred and Alternative Lexical Labels

**Notes:**
“Animals” has a preferred label = animals
“Animals” has an alternate label = creatures
Creatures SEE Animals
Animals USE FOR Fauna

## Registries: the Big Picture

- (Adapted from Wagner & Weibel, “The Dublin Core Metadata Registry: Requirements, Implementation, and Experience” JoDI, 2005)

**Notes:**
How does all of this encoding of these concepts, etc. work?
Coupled with registries, it allows you to do certain things.
We’ve been able to support traditional library vocabularies because of institutional commitments.  But for others, use registries.
Between Interoperate & Discover:  Express 
Between Standardize and Interoperate:  Register
Could use MADS or SKOS to expose vocabularies.  SKOS is better for subjects and handles concepts well.
E-Card is an encoding standard for names
If you want vocabularies to be available for use by others, you must document and publish them.

## Why Registries?

- Support interoperability
- Discovery of available schemes and schemas for description of resources
- Promote reuse of extant schemes and schemas
- Access to machine-readable and human-readable services
- Support for crosswalking and translation
- Coping with difference metadata schemes

**Notes:**
You can (should) register metadata schemes, values used in your controlled vocabularies.
Support for crosswalks and translations– because this is a standard way to express versioning.

## Declaration, documentation, publication

- To identify the source of a vocabulary, e.g., a term comes from LCSH, as identified in my metadata by a URI: info:lcsh
- To clarify a term and its definition
- To publish controlled vocabularies and have access to information about each term

**Notes:**
These are the pieces that are important for building the infrastructure that makes good metadata function.
Most of these steps can be done by anyone, without support from LC, OCLC, etc.  We are building up our own support this way– distributed support, not centralized support. Even the registry portions (still being built) will be distributed.

## Some uses for registries

- Metadata Schemas
- Crosswalks between metadata schemas
- Controlled Vocabularies
- Mappings between vocabularies
- Application Profiles
- Schema and vocabulary information in combination

**Notes:**
Most of the registries extant (DC, for instance) register schemas primarily (though the DCMI Registry includes the 12 term DCMIType vocabulary), but the intention is that all these pieces need registration and management for the benefit of all.

## Metadata registries

- Some are formal, others are informal lists
- Some formal registries:
- Dublin Core registry of DC terms
- NSDL registry of vocabularies used
- LC is establishing registries
- MARC and ISO code lists
- Enumerated value lists
- LCSH in SKOS

## Applying metadata standards: quality issues

- Defining quality
- Criteria for assessing quality
- Levels of quality
- Quality indicators

## Determining and Ensuring Quality

- What constitutes quality?
- Techniques for evaluating and enforcing consistency and predictability
- Automated metadata creation: advantages and disadvantages
- Metadata maintenance strategies

**Notes:**
Quality in the metadata world is not generally the same as in traditional libraries.  It’s important that we re-start the conversation about quality with these differences in mind.  We want to talk about ...

## Quality Measurement: Criteria

- Completeness
- Accuracy
- Provenance
- Conformance to expectations
- Logical consistency and coherence
- Timeliness (Currency and Lag)
- Accessibility

**Notes:**
Completeness: describe the item as completely as is economically feasible
Accuracy: apply standards consistently with minimum of error
Provenance: provide information on source of metadata, whether it was generated or converted
Conformance to expectations: reflects what the given community would expect, using appropriate elements and controlled vocabulary
Logical consistency: uses application profiles and similar structures
Timeliness: when the target item changes the metadata may need revision
Accessibility: make sure metadata is openly available (if possible) and links are current; should be accessible by people with disabilities

## Basic Quality Levels

- Semantic structure (“format,” “schema” or “element set”)
- Syntactic structure (administrative wrapper and technical encoding)
- Data values or content

## Quality Indicators: Tier 1

- Technically valid
- Defined technical schema; automatic validation
- Appropriate namespace declarations
- Each element defined within a namespace; not necessarily machine-resolvable
- Administrative wrapper present
- Basic provenance (unique identifier, source, date)

**Notes:**
Bottom tier of what you should have.
Unique ID for the metadata itself, which is not the same as the Identifier for the object.

## Quality Indicators: Tier 2

- Controlled vocabularies
- Linked to publicly available sources of terms by unique tokens
- Elements defined and documented by a specific community
- Preferably an available application profile
- Full complement of general elements relevant to discovery
- Provenance at a more detailed level
- Methodology used in creation of metadata?

**Notes:**
This is a step up.  You know which value is from which vocabulary and are using it properly.
Examples of tokens are:  LCSH, AAT, etc.
We’ll come to this documentation piece over and over
At this stage, you will assure that your data “plays well with other data” in regular aggregation.
“On the Horse” problem:  In the Theodore Roosevelt collection, none of the objects actually had the name of Theodore Roosevelt in the title.  Instead, they had titles, like “On the horse” because of course they were all about T.R.  This happens all the time!
At this stage, there is come indication of the methodology of how the metadata was created:  by machine or by a person.

## Quality Indicators: Tier 3

- Expression of metadata intentions based on documented AP endorsed by a specialized community and registered in conformance to a general metadata standard
- Source of data with known history of updating, including updated controlled vocabularies
- Full provenance information (including full source info), referencing practical documentation

**Notes:**
We’re not here yet.  Hardly anyone is doing this

## Improving Metadata Quality ...

- Documentation
- Basic standards, best practice guidelines, examples
- Exposure and maintenance of local and community vocabularies
- Application Profiles
- Training materials, tools, methodologies

**Notes:**
Question is about how to provide information to people who are using and creating your data.
Assumptions in the cataloging community:  of course everyone is using AACR2, etc.
That is not true in the metadata world– you cannot make those assumptions.
How to expose local, community vocabularies properly?