A Data Science Definition of Semantics

Semantics is the study of meaning (Greek root, semantikos, signficant meaning). In the context of data science, semantics is the technique of creating meaningful assertions about data objects. A meaningful assertion, as used here, is a triple consisting of an identified data object, a data value, and a descriptor for the data value. In practical terms, semantics involves making assertions about data objects (ie, making triples), combining assertions about data objects (ie, merging triples), and assigning data objects to classes; hence relating triples to other triples. As a word of warning, few informaticians would define semantics in these terms, but most definitions for semantics are functionally equivalent to the definition offered here.

Most language is unstructured and meaningless. Consider the assertion: Sam is tired. This is an adequately structured sentence with a subject verb and object. But what is the meaning of the sentence? There are a lot of people named Sam. Which Sam is being referred to in this sentence? What does it mean to say that Sam is tired? Is "tiredness" a constitutive property of Sam, or does it only apply to specific moments in Sam's life? If so, for what moment in time is the assertion, "Sam is tired" actually true? To a computer, meaning comes from assertions that have a specific, identified subject associated with some sensible piece of fully described data (metadata coupled with the data it describes).

- Jules Berman (copyrighted material)

key words: meaning, data science, informatics, triple, semantics, jules j berman

TRIPLES: THE BASIC UNIT OF MEANING IN DATA SCIENCE

Data, by itself, has no meaning. It is the job of the data scientist to assign meaning to data, and this is done with data objects, triples, and classifications. Our most familiar data constructions (eg, spreadsheets, relational databases, flat-file records) convey meaning through triples and data objects; we just don’t perceive them as such.

The three conditions for a meaningful assertion are:

1. There is a specific data object about which the statement is made.
2. There is data that pertains to the specified object.
3. There is metadata that describes the data

Simply put, assertions have meaning whenever a pair of metadata and data (the descriptor for the data and the data itself) is assigned to a specific object. In the informatics field, assertions come in the form of so-called triples, consisting of the object, then the metadata, and then the data.

Here are some examples of triples, as they might occur in a medical dataset:

"Jules Berman" "blood glucose level" "85"
"Mary Smith" "blood glucose level" "90"
"Samuel Rice" "blood glucose level" "200"
"Jules Berman" "eye color" "brown"
"Mary Smith" "eye color" "blue"
"Samuel Rice" "eye color" "green"

Here are a few triples, as the might occur in a haberdasher’s dataset

"Juan Valdez" "hat size" "8"
"Jules Berman" "hat size" "9"
"Homer Simpson" "hat size" "9"
"Homer Simpson" "hat_type" "bowler"

We can combine the triples from a medical dataset and a habderdasher’s data set that apply to a common object:

"Jules Berman" "blood glucose level" "85"
"Jules Berman" "eye color" "brown"
"Jules Berman" "hat size" "9"

Triples can port their meaning between different databases because they bind described data to an object. The portability of triples permits us to achieve data integration of heterogeneous data, and facilitates the design of software agents. Data integration involves merging related data objects, across diverse data sets. As it happens, if data supports introspection and data is organized as meaningful assertions (ie, as identified triples), then data integration is implicit (ie, an intrinsic property of the data). In essence, data integration is awarded to data scientists who apply data simplification techniques.

- Jules Berman (copyrighted material)

key words: triple, meaning, informatics, computer science, data integration, heterogeneous data, jules j berman

THE MEANING OF MEANING

In informatics, meaning is achieved when described data is bound to a unique identifier of a data object. The assertion that "Claude Funston’s height is five feet eleven inches," comes pretty close to being a meaningful statement. The statement contains data (five feet eleven inches), and the data is described (height). The described data belongs to a unique object (Claude Funston). Ideally, the name "Claude Funston" should be provided with a unique identifier, to distinguish one instance of Claude Funston from all the other persons who are named Claude Funston. The statement would also benefit from a formal system that ensures that the metadata makes sense (eg, What exactly is height, and does Claude Funston fall into a class of objects for which height is a property?) and that the data is appropriate (eg, Is 5 feet 11 inches an allowable measure of a person’s height?). A statement with meaning does not need to be a true statement (eg, The height of Claude Funston was not 5 feet 11 inches when Claude Funston was an infant).

© 2016 Jules Berman

key words: meaning, semantics, assertion, triple, rdf, metadata, identifier, identification, jules j berman

More Introduction to RDF

As discussed in an earlier post, RDF (Resource Description Framework) is a formal method for describing specified data objects with paired metadata and data.

It is important to understand that in informatics, assertions only have meaning when a pair of metadata and data (the descriptor for the data and the data itself) is assigned to a specific subject.

The "triples" that form the basis of RDF specifications are: Specified subject then metadata then data

Examples of triples that might be found in a medical dataset:

“Jules Berman” “blood glucose level” “85”
“Mary Smith” “blood glucose level” “90”
“Samuel Rice” “blood glucose level" "200"
“Jules Berman” “eye color” “brown”
“Mary Smith” “eye color” “blue”
“Samuel Rice” “eye color" "green"


Some triples found in a haberdasher's dataset

“Juan Valdez” “hat size” “8”
“Jules Berman” “hat size” “9”
“Homer Simpson” “hat size” “9”
“Homer Simpson” “hat_type” “bowler”

Triples collected from both datasets whose subject is "Jules Berman"

“Jules Berman” “blood glucose level” “85”
“Jules Berman” “eye color” “brown”
“Jules Berman” “hat size” “9”

This is a simple example of data integration over heterogeneous datasets!

Triples can port their meaning between different databases because they bind described data to a specified subject. This supports data integration of heterogeneous data and facilitates the design of software agents. A software agent, as used here, is a program that can interrogate multiple RDF documents on the web, initiating its own actions based on inferences yielded from retrieved triples.

RDF (Resource Description Framework) is a syntax for writing computer-parsable triples. For RDF to serve as a general method for describing data objects, we need to answer the following four questions:.

1. How does the triple convey the unique identity of its subject? In the triple, “Jules Berman” “blood glucose level” “85”, The name "Jules Berman" is not unique and may apply to several different people.

2. How do we convey the meaning of metadata terms? Perhaps one person's definition of a metadata term is different from another person's. For example, is "hat size" the diameter of the hat, or the distance from ear to ear on the person who is intended to wear the hat, or a digit selected from a pre-defined scale?

3. How can we constrain the values described by metadata to a specific datatype? Can a person have an eye color of 8? Can a person have an eye color of "chartreuse"?

4. How can we indicate that a unique object is a member of a class and can be described by metadata shared by all the members of a class?

In subsequent blog posts, we'll examine how RDF provides answers to these four questions.

-Jules Berman tags: data integration, meaning, rdf, specifications, standards, triples, science

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Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.