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  1. Introduction
  2. Artificial Intelligence and the Semantic Web: Difference Between
  3. RDF (Resource Description Framework)
    1. Metadata
    2. Data Model
    3. Abstraction
    4. RDF
    5. Subject-Predicate-Object Triple Stores
    6. Uniform Resource Identifier
  4. Infrastructure Technology
    1. XML (Extensible Markup Language)
    2. OWL (Web Ontology Language)
    3. SPARQL
  5. RDS/WIP (Reference Data System / Work In Progress)
    1. RDL (Reference Data Library)
  6. Gellish
  7. Ontology and Taxonomy: Difference Between
    1. Taxonomy
    2. Generalization/Specialization
    3. Subtype/Supertype
    4. Ontology
  8. First Order Logic
  9. Some More Terms
    1. Semantic
    2. Syntactic
    3. Reuse
    4. Encapsulate
  10. Current Proponents of Interoperability
    1. EPRI (Electric Power Research Institute)
    2. ARC (ARC Advisory Group)
    3. MIMOSA (Machinery Information Management Open Systems Alliance)
    4. IOHN (Integrated Operations in the High North)
  11. Next


There are a great many glossaries available, as well as on-line dictionaries and, of course, Wikipedia. Here are three:

So the world does not need another complete listing of computer terminology. But to save your time searching, the following are terms that are particularly interesting to the study of ISO 15926.

Artificial Intelligence and the Semantic Web: Difference Between

Artificial Intelligence - Quick 'n Dirty

Making machines smarter

Semantic Web - Quick 'n Dirty

Making data smarter

We all want to be able to find and use information on the World Wide Web easier and more reliably. The Artificial Intelligence approach is to make machines smarter by teaching them to infer the meaning of web data by using techniqes such as natural language and image processing. In contrast, the Semantic Web approach is to make the data itself smarter (that is, by making the data easier for machines to find, access, and process) by using techniques for expressing data and meaning in a standard machine-readable format.

ISO 15926 uses some Semantic Web technology to describe plant objects in a way that computers can understand.

Suggested Reading

RDF (Resource Description Framework)

If you dig deeper under the hood of ISO 15926 you will soon run into this term because it is the means of storing the Part 4 definitions.

Wikipedia says that Resource Description Framework is a set of specifications originally designed as a metadata data model. But if you are like the author, this doesn't help at all, so we will deconstruct the definition.


  • Metatdata is data about data. For instance, one piece of metadata about the ISO 15926 Primer is that it was written on the POSC/Caesar wiki website.

Data Model

  • A data model is an abstract model that describes how data is represented and accessed.


  • Abstraction is a process of generalizing about something to reduce the information content about an object to only those attributes you are interested in. A typical abstraction is the answer 7600 Glover Road to the question "Where do you live?" You might live in a beautiful split level house with a wonderful view of the ocean framed by huge 100 year old pine trees but your questioner only wants to know where to have a package delivered. (On the other hand, yours could be a very ordinary house on a very ordinary road, but the city just wants your land for a freeway bypass and the friendly bulldozer operator needs to know where you live.)


Putting it all together, then, RDF is:

  • instructions on how to represent
  • just the bits of data you are interested in
  • that describes certain other bits of data
  • then access it easily

(Whew! I bet you thought that was going to be difficult!)

In particular, RDF makes statements about things, which it calls Resources, in the form of Subject-Predicate-Object expressions known as Triple Stores.

Subject-Predicate-Object Triple Stores

"The ISO 15926 Primer was written on the POSC/Caesar wiki" might be stored in the RDF as the triple:

  • the subject: ISO 15926 Primer
  • the predicate: was written on
  • the object: POSC/Caesar wiki

The each term in the subject-predicate-object triple may be explicitly named, as in the example above, or they could be in the form of a URI, a Uniform Resource Identifier.

Uniform Resource Identifier

You can think of a Uniform Resource Identifier as a website for a piece of information. This allows the same resource to be reliably referenced many times. So instead of writing the Subject-Predicate-Object triple as above, it could be rendered as:

And in fact we could carry this further by defining somewhere on the Internet the exact meaning of the phrase was written on, and put its URI in the predicate.

Suggested Reading

If you really want to know more about RDF, here are a few places to start. Be warned: these are a bit daunting and are not for the feint of heart.

    A summary of the development of RDF. It includes more references in case you havn't had enough yet.

Infrastructure Technology

If the RDF material above put you to sleep don't say I didn't warn you. (I seem to remember using the word "daunting".) The first question to ask is whether or not you actually need to understand it. Most users of 15926 will not even have to know how to spell "RDF", let alone know what the initials stand for.

If you do want to perservere, a good place to start is to learn about the enabling technology. The abstract (remember that word?) RDF model can be implemented in a number of ways. The sections below describe the technology used by ISO 15926.

XML (Extensible Markup Language)

ISO 15926 uses Extensible Markup Language as a transport language. With XML, information is written in a manner that allows machines to know what the data values represent. Like HTML, XML uses tags bounded by "<" and ">", but the tags in XML are intended to describe what the data is, rather than how to render it on a computer screen. Thus, the appearance of an XML document is not generally important.

OWL (Web Ontology Language)

OWL is actually a family languages for creating ontologies. It is fundamental to the Semantic Web. OWL ontologies are usually expressed using RDF/XML syntax.

Suggested Reading


SPARQL, pronounced "sparkle", is a query language designed to be used with RDF triple stores. It's name is self referencing, "SPARQL Protocol and RDF Query Language". The ISO 15926 RDS/WIP uses SPARQL.

Suggested Reading

RDS/WIP (Reference Data System / Work In Progress)

The RDS/WIP is several things:

  • a library of reference data for ISO 15926
  • a means of publishing core ISO 15926 definitions
  • a platform for developing new ISO 15926 definitions
  • a workspace for harmonizing other standards with ISO 15926 (or each other)

The RDS/WIP is a large triple store in the form of Subject-Predicate-Object. It uses semantic web technology (OWL, RDF, and SPARQL) over top of a conventional web technology such as HTTP to provide machine-oriented access to the stored definitions. A conventional HTML presentation is used to provide a human-oriented interface to the same system.

Anyone can search the RDS/WIP and find terms, much like in a dictionary. Accredited users can add information to the RDS/WIP.


RDL (Reference Data Library)

POSC Caesar has it's own library of reference data (hence: RDL) for ISO 15926-4.


Gellish (originally derived from General Engineering Language) is a language in which information can be expressed in a manner that is computer readable. It is used to make the ISO 15926-7 templates.


Ontology and Taxonomy: Difference Between

Taxonomy - Quick 'n Dirty

If you've ever made a classified list of all your CDs, you've made a taxonomy. (But if you're as old as the author, CDs are old hat. You learned how to do this years ago with your player piano rolls!") And if you've ever had to grapple with the question of where to classify Weird Al (under "Parody?", "Rock and Roll?", or "Idiot?"), you've come up against the idea of single or multiple inheritance!

Ontology - Quick 'n Dirty

If you've ever played the parlor game Twenty Questions, you intuitively understand ontology. In this game you more-or-less start with an Ontology-of-Everything-In-The-World, and with each successive question ("Is it a ...?") apply a more limited ontology as a filter (usually starting with "Is it an Animal, Vegetable, or Mineral?") The game ends when there is only one object left, The Answer, that satisfies membership (or non-membership in the case the answer to "Is it a ...?" is "No!") in all the ontologies.

Ontology and Taxonomy are both terms in a continuum that some information scientists call Knowledge Organization Systems (KOS). And just to confuse you some more, the continuum incudes Thesaurus, Controlled Vocabulary, and Faceted Classification among many other things. The bad news for those of you not used to dealing with ambiguity (All you mechanical engineers out there: Raise your hands!) is that there is a great deal of overlap in those terms. Even people who's job it is to know these things (All you mechanical engineers out there: Put your hands down!) can't give a short answer when asked where the boundaries are.


A taxonomy is a collection of terms that have explicit definitions that have been organized into a hierarchical structure. They tend to be organized in tree-like structures that are reasonably easy to understand, even by non-specialized people. Each term is related to its parent in a is-a-kind-of relationship.

For instance, a car is-a-kind-of automobile. But a car also is-a-kind-of machine, so if your taxonomy is concerned with machines, you should analyze the relative order of these three things. Depending on the purpose of your taxonomy, you will likely end up with:

  • car is-a-kind-of automobile, which is-a-kind-of machine.


The is-a-kind-of relationship is known as generalization/specialization. In the above example a car is a specialization of automobile; automobile is a generalization of car.


Subtype/supertype is just another way of saying generalization/specialization. So continuing the example above, car is a subtype of automobile; automobile is a supertype of car. The understanding is that the subtype has all the constraints of the supertype, plus one or more additional constraints.


In the realm of philosophy, ontology is the study of being; the study of the things that are. In the realm of information science (which is where ISO 15926 firmly resides), ontology has a more formal meaning. Wikipedia says that an ontology is "a formal representation of a set of concepts within a domain and the relationships between those concepts."

Like Taxonomies, ontologies are also arranged in a is-a-kind-of relationship, but the relationships tend to be more richly defined. The difference is subtle. One commentator compared the difference between ontology and taxonomy to your computer hard disk. The taxonomy would be the directory structure without the files, while the ontology would be the files organized by the directory structure.

Earlier in this Primer, we talked about an Ontology of Things That Will Carry a Bicycle. This Ontology is the whole collection of things that will carry a bicycle in case the author's bicycle breaks down on the way to work. Each object in the ontology would have a Taxonomy that you could examine.


If you want to see how deep the subject of Ontology is, here are two links.

Professor Dagobert Soergel of the University of Maryland has written an explanation of Knowledge Organization Systems:

A number of ontology professionals have formed an organization called Ontolog. It is devoted to advancing the field of ontology, ontological engineering, and semantic technology. The ogranization hosts regular lectures via conference call that are open to anyone. Links to past lectures are maintained on their website.

First Order Logic

First Order Logic - Quick 'n Dirty

If you've ever taken a mathematics course where you have had to prove something, you've used first order logic.

First order logic is used in ISO 15926 as a basis for developing the classes, which make up Part 4, and the templates, which make up Part 7. If you are looking for an introduction, Wikipedia is hard to beat.

Some More Terms


Semantics - Quick 'n Dirty

If you've ever read Alice's conversation with Humpty Dumpty, you've had a lesson in semantics. An excerpt:

Humpty Dumpty: "...How old did you say you were?"
Alice made a short calculation, and said "Seven years and six months."
"Wrong!" Humpty Dumpty exclaimed triumphantly. "You never said a word like it!"
"I thought you meant 'How old are you?'" Alice explained.
"If I'd meant that, I'd have said it," said Humpty Dumpty.

Semantics has to do with meaning. Sometimes the word is used derisively, as in ...Yes, but that's only semantics. But in ISO 15926 semantics is everything. Elsewhere in this Primer we have talked about embedding context with the data. What we mean by this is capturing the semantics.

Semantic precision means that a precise meaning, neither no more or no less, can be had. For instance, in a field of engineering there might be many versions of the word temperature. A user of any of the versions must be able to use each version reliably to convey the correct meaning.

Semantic fidelity is used when describing information exchanges. We are looking for high semantic fidelity to make sure the meaning of data values is preserved in the receiving end.


Syntax is concerned with structure and addressability. That is, the position of a word in a formal logic statement affects its meaning.


Reuse is a central idea in ISO 15926, in that once a compliant system for exchanging information is set up, it can be reused an infinite number of times at no extra cost. This runs counter to the notion of point-to-point mapping to achieve interoperability, which is application-specific (and often version-specific) and cannot be reused for more than its original design.


Hiding complexity from users who really don't want to know any more.

Current Proponents of Interoperability

EPRI (Electric Power Research Institute)

From its website: "The Electric Power Research Institute, Inc. (EPRI) conducts research and development relating to the generation, delivery and use of electricity for the benefit of the public..."

One of EPRI's mandates is to promote cooperation between its members. One aspect of cooperation is the easy interoperability of digital information.



ARC (ARC Advisory Group)

From it's website: "ARC Advisory Group is a research and advisory firm for manufacturing, energy, and supply chain solutions..."

ARC is a frequent participant in Asset Lifecycle conferences.



MIMOSA (Machinery Information Management Open Systems Alliance)

From their website: "MIMOSA is a not-for-profit trade association dedicated to developing and encouraging the adoption of open information standards for Operations and Maintenance in manufacturing, fleet, and facility environments. MIMOSA's open standards enable collaborative asset lifecycle management in both commercial and military applications."


IOHN (Integrated Operations in the High North)

Oil and gas operations in the high north are remote sites, leading to heavy demands on communication. Instrumentation and efficient transfer of real time data between fields and centralized operations is critical to profitable development.




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