Once you are finished with your definition you’ll need to place the new term into context with other terms. It allows your readers to see how terms interact with each other. It allows Natural Language Processing Engines to relate terms together. It is the core in pattern-matching for harmonizing regulatory structures to each other.
The following basic relationships have been taken from the Simple Knowledge Organization System’s (SKOS) Mapping Vocabulary Specification, as shown below.
They offer the ability to distinguish subtle relationships between two terms. As stated in the specification, “Many knowledge organization systems, such as thesauri, taxonomies, classification schemes and subject heading systems, share a similar structure, and are used in similar applications. SKOS captures much of this similarity and makes it explicit, to enable data and technology sharing across diverse applications.”
If two concepts are an exact match, then the set of resources properly indexed against the first concept is identical to the set of resources properly indexed against the second. Therefore, the two concepts may be interchanged in queries and subject-based indexes. (Is inverse with itself.)
If “concept A has-broad-match concept B,” then the set of resources properly indexed against concept A is a subset of the set of resources properly indexed against concept B. (Is inverse of has-narrow-match.)
If “concept A has-narrow-match concept B,” then the set of resources properly indexed against concept A is a superset of the set of resources properly indexed against concept B. (Is inverse of has-broad-match.)
If “concept A has-major-match concept B,” then the set of resources properly indexed against concept A shares more than 50% of its members with the set of resources properly indexed against concept B. (No inverse relation can be inferred.)
If “concept A has-minor-match concept B,” then the set of resources properly indexed against concept A shares less than 50% but greater than 0 of its members with the set of resources properly indexed against concept B. (No inverse relation can be inferred.)
The problem in the SKOS model is relationships are limited to a single term or a single phrase. This model is great if you want to know that draft or chart is the same as map or not as broad as interpret. Basically, you are limited to three categories for practical purposes; broader, same, and narrower as shown in the diagram below.
What the SKOS and basic semantic relationship model doesn’t tell you is why interpret is a broader concept, or why scale is a narrower concept. What they don’t show are the linguistic relationships between the terms.
To extend the relationships past broader, same, and narrower, you’ll need a more advanced semantic relationship system. It should consider real world relationships such as one concept being a category for another concept, or one concept enforcing another concept, or even one concept including another concept as a part of it (versus the parent being a category). The illustration that follows re-examines the semantic relationships of the term map, shown above, using a more advanced set of semantic relationships. These relationships provide a much more robust understanding of connecting terms than a simple broader, same as, and narrower model can provide. Advanced semantic relationships extend the model by adding linguistic and conceptual connections to each relationship.
There are many more relationships you’ll need to put into place if you want to provide greater context for your readers or Natural Language Processing Engine. Here are a few more of the relationships you’ll need.
Synonyms are broader than exact matches, as they extend the relationship to facts or states of having correlation, interrelation, materiality, conformity, and pertinence between concept A and concept B. And antonyms then have enough variability, incongruence, and disassociation to be their opposite. The antonym is the inverse of the synonym and vice versa.
Included in the type of synonyms is metonymy, the semantic relationship that exists between two words (or a word and an expression) in which one of the words is metaphorically used in place of the other word (or expression) in particular contexts to convey the same meaning.
Included in the category of antonyms are complementary pairs, gradable pairs, and relational opposites.
Complementary pairs are antonyms in which the presence of one quality or state signifies the absence of the other and vice versa. A couple of samples are single/ married, not pregnant/pregnant. There are no intermediate states in complementary pairs.
Gradable pairs are antonyms which allow for a natural, gradual transition between two poles. A couple of examples are good/bad, hot/cold. It is possible to be a little cold or very cold, etc.
Relational opposites are antonyms which share the same semantic features, only the focus, or direction, is reversed. A couple of examples are tied/untied, buy/sell, give/receive, teacher/pupil, father/son, and open/refrain from opening.
A spigot and a faucet are two defined words that are exact matches, or synonyms, of each other. That’s an easy rule to implement. However, language is messy, and the uses of language within compliance documents is even messier. That’s why you must have advanced rules that go beyond synonyms for use cases such as a personal data request being called a request for personal data, an information request from the data controller, or even a request for information on the processing of personal data. To handle these types of use cases you must have a semantic rule that says “if the definition of a term-of-art matches the definition of a previously accepted dictionary term, the term-of-art should be considered an exact match and therefore be labelled a non-standard representation of the accepted term”.
The major and minor relationships described in the SKOS model are limited to linguistic parents and their children (or half children as a minor match might be thought of). However, there are many relationships that are more specific that can and should be applied, especially when working with named entities and leveraging a Natural Language Processor’s named entity recognition engine. By replacing the simple broader and narrower matches with more specific categorization, you can achieve structures like those employed by the Compliance Dictionary, as shown below.
|Linguistic Parent||Terms that are linguistically broader than the focus term, including origins of terms.||Term– Senior Systems Analyst|
|Linguistic Parent – systems analyst, senior|
|Linguistic Child||Terms that are linguistically narrower than the focus term, including derivatives. This is the in-verse of Linguistic Parent.||Term - systems analyst|
|Linguistic Child - Senior Systems Analyst|
|Category For||A term of which the focus term is a kind of.||Term – tablet|
|Category For – portable electronic device|
|Type of||Terms that are kinds or examples of the focus term. This is the inverse of Category For.||Term – portable electronic device|
|Type of – laptop|
|Includes||Terms the focus term is an element of. It is the same as hyponymy.||Term – Personally Identifiable In-formation|
|Includes – mailing address, individual’s Social Security Number|
|Part of||Terms whose definitions are an element of the focus term. This is the inverse of Includes||Term – Personally Identifiable Information|
|Part of – privacy related information|
|Used to Create||A term that is a template for or used to create the focus term.||Term – UCF Mapper software|
|Used to Create – Authority Document mapping|
|Is Created by||A term that is comes from or is generated by the focus term. This is the inverse of Used to Create.||Term – system audit report|
|Is Created by – Secure Configuration Management Tool|
|Is Referenced by||A term that mentions or references the focus term.||Term – evidence|
|Is Referenced by – probable cause|
|References||A term that the focus term mentions or cites. This is the inverse of Is Referenced by.||Term – evidence|
|References – business exception rule|
|Used to Enforce||A term that uses the focus term to happen or cause compliance.||Term – configuration rule|
|Used to Enforce – system configuration|
|Is Enforced by||A term that uses the focus term to happen or cause compliance. This is the inverse of Used to Enforce.||Term – PCI-DSS|
|Is Enforced by – payment brand|
|Used to Prevent||A term that prevents the focus term.||Term – sanctions|
|Used to Prevent -– unauthorized data processing|
|Is Prevented by||A term that is prevented by the focus term. This is the inverse of Used to Prevent||Term – stealing|
|Is Prevented by – armed guard|
As of this writing, there isn’t a computer system that will automatically analyze terms, even in their context within a document, and determine what the relationships should be. At best, they are running between 40-45% accurate. This means you’ll want to manually ask yourself the questions, which isn’t really that hard. Here’s our cheat sheet for you.
|Synonyms||Have you seen this term spelled differently?|
|Have you seen this term written completely different (Personally Identifiable Information/individual’s non-public data)?|
|Is this a metaphor for another term?|
|Are there metaphors for this term?|
|Antonyms||Are there any qualities of this term that signify the absence of qualities of another term (single/married)?|
|Could this term be graded on a spectrum (hot/cold)?|
|Is there an opposite relationship of this term (tied/untied)?|
|Category of||What terms fall under this category?|
|Type of||Are there any other examples of this term?|
|Includes||What does this term include?|
|Part of||Is this term a part of a greater whole?|
|References||Does this term refer to other terms?|
|Is this term referenced by other terms?|
By creating semantic relationships to your definitions, the reader will be able to understand how the term works with other terms.
 https://www.w3.org/2004/02/skos/mapping/spec/ and https://www.w3.org/TR/skos-reference/
 “Linguistics 201: Study Sheet for Semantics.”
 Malaise, Zweigenbaum, and Bachimont, “Detecting Semantic Relations between Terms in Definitions.”
 Storey, “Understanding Semantic Relationships.”