edits

Vocabulary-properties-and-ontologies.md

@@ -9,9 +9,13 @@ An ontology is a heirarchy of controlled vocabulary terms that explicitly encode
 Using the example of subject (aka "topical subject"), either technology allows us to make assertions about the
 data and relations between identities. 
 
+- Both technologies can be used simultaneously to describe identities, however, doing double data entry would
+  be irksome.
+
 - Ontolgies allow explict assertions, and stronger assertions
 
-- Both technologies are weak if a subject is missing, that is, the identity was not marked up (to use XML-speak)
+- Both technologies are weak if a subject is missing, that is, the identity was not marked up (to use
+  XML-speak; in fact we are using a database and creating relational links)
 
 - More extensive is better, but the extent of ontology or vocabulary is limited by resources
 
@@ -19,8 +23,8 @@ data and relations between identities.
 
 - Flat vocabularies are less difficult to create and maintain (perhaps much less difficult)
 
-- Both vocabularies have an implicit definition for each term; however, two different editors may understand
-  somewhat different implied defintions
+- Both technologies have an implicit definition for each term; however, two different humans (editors,
+  scholars) may understand somewhat different implied defintions
 
 - A single explicit definition can be added to each term (although I haven't seen this; it may only exist in
   fields outside the archival world)
@@ -31,13 +35,17 @@ data and relations between identities.
 
 - An ontology requires at least 2 database tables, perhaps 3
 
-- Policies need to be developed for create, update, and delete
+- Policies need to be developed for create, update, and delete of terms in either technology
 
 - Policy complexity is greater for an ontology
 
 - Using computers, an identity may have multiple subjects of either ontology, or flat vocabulary
 
-It might also be sensible to design the properties with multilingual vocabulary terms. By multilingual I mean:
+- Building either technology can (and almost certainly) will be an on-going process. We don't have to start
+  with a fully mature vocabulary. That said, records edited early in the life of the data will be somewhat
+  less-well-marked-up than records marked up later.
+
+It might also be sensible to design the terms with multilingual vocabulary terms. By multilingual I mean:
 multiple terms for each unique ID where each term is specific to a specific language, and all terms with the
 same ID share a definition. 
 
@@ -48,7 +56,7 @@ The flat vocabulary has a single "type" for each term, where type examples are:
 function, etc. In an ontology, the "type" is handled by the ontology structure, which is explicit, but
 discovering the type requires tree-traversal.
 
-### Property domain
+### Term domain
 
 
 Intellectually each property has a definition. Technically, dding an explicit definition only required an
@@ -57,22 +65,22 @@ intellectually. The Wikipedia clarifies this issue since ambiguous terms lead to
 page. Wikipedia "definition" is the article.
 
 
-### Proper entities are not properties
+### Proper entities are not terms
 
-There is no property "detroit", although there is a CPF entity for "Detroit, MI USA", complete with a field
+There is no term "detroit", although there is a CPF entity for "Detroit, MI USA", complete with a field
 for the corresponding geonames ID. It is technically possible to conflate CPF entities in the user interface
 to enable the construction of a topical subject "detroit", although that intellectually sub-optimal. The data
 should be as well-constructed as possible. A search for subject + place is not a search for subject +
 subject(placename).
 
-Consider what happens if (and I'm opposed to this) all CPF entities were imported into the property
+Consider what happens if (and I'm opposed to this) all CPF entities were imported into the term
 table. That would be denormalization, data duplication, and would only end in tears.
 
 
 ### Use Markov models instead of an ontology
 
 Ontologies are difficult to create, and there is disagreement about them, both in structure and content. There
-are several to choose from, the the properties they use are somewhat incomplete as confusing. Linking (aka
+are several to choose from, the the terms they use are somewhat incomplete and confusing. Linking (aka
 markup of) each aspect of an identity record's properties to the ontology is an onerous task, and fraught with
 several types of errors. Linking is often a judgement call.
 
@@ -80,7 +88,7 @@ A technology exists that is easy to implement, powerful, and tractable in real l
 vocabularies.
 
 We can create a Markov matrices of the terms. Multiplying Markov matrices causes them to converge which
-reveals property relatedness as exists in the data. The effect is quite powerful and (almost?) obviates the
+reveals term relatedness as exists in the data. The effect is quite powerful and (almost?) obviates the
 need for a hand-created ontology. Missing relations (known to exist, but not discovered by the Markov
 convergence because no records actually contain the desired relation) are easily rectified by either of two
 methods. The first would be to add the correct relations to existing records. The second works by creating
@@ -97,7 +105,7 @@ http://www.youtube.com/watch?v=WHeta_YZ0oE
 http://www.youtube.com/watch?v=x3wOhXsjPYM
 
 
-### Ontology uses property, but is a separate problem
+### Ontology uses terms, but is a separate problem
 
 The alternative to the Markov relation discovery is an ontology that relates terms both in relatedness,
 and as a hierarchy from broad to narrow. There are existing ontologies with varying levels of detail.
@@ -113,7 +121,7 @@ Painting subject:Automobiles subject:Painting (fine art)
 ```
 
 The underlying terms are the same in both. However, the ontological relationship is quite different
-because one is a corporate body, and the other is an art object. It is not the domain of a flat property to
+because one is a corporate body, and the other is an art object. It is not the domain of a flat term to
 know how it is applied to a database record. Also, the larger context of what is being described changes how
 the description is perceived. In any case, the use of a flat vocabulary is sufficient for search and discovery, and
 Markov matrices can discover relatednesss between records. Hierarchy becomes another type of relatedness. In
@@ -123,17 +131,19 @@ identities in the database with both "Automobiles" and "Engineering" as subjects
 The example above is limited to terms as topical subject. It seems reasonable to add fields in order to apply
 additional terms (beyond "topical subject") "typeOf" or "isA", while still using the same (original, large)
 list of terms. Types of "publisher (corporateBody)" and "painting (object)" seem obvious. Applying both
-property and type pairs will explicitly categorize any database record, even without using an
+term and type pairs will explicitly categorize any database record, even without using an
 ontology. However, it is unclear how this somewhat loosely coupled description will impact being able to
 reason about database records. This also requires adding fields to the CPF database schema, which carries
 serious baggage.
 
 
-### Ontology and property interact to create search facets
+### Ontology and term interact to create search facets
 
-In general, a search for a parent property should include all child properties as specified by the
-ontology. Searching for the Spanish term "ropa" (clothes) will include "cinturon" (belt) which has the English
-term "belt (clothing)". This works well as long as the ontology is complete. 
+In general, a search for a parent term should include all child terms as specified by the ontology. A
+multilingual example would be searching for the Spanish term "ropa" (clothes) will include "cinturon" (belt)
+which has the English term "belt (clothing)". This works well as long as the ontology is complete. Note that
+being a controlled vocablulary, the Spanish "ropa" has the dsame ID as English "clothes", and the search is
+performed based on ID number, not text string.
 
 Interestingly, we might be able to apply Markov matrices to identities marked up via ontology, with the same
 sort of relatedness building that occurs with a flat vocabulary list.