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Learner: Mark Crayton
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RSH9101
Research topic, problem, purpose, and
questions
Learner Comments:
Faculty Use Only
Activity 8: Topic Paper
Running head: SOLVING BLACK INEQUALITY USING ONTOLOGIES
Information Scientist’s View of Solving Black American Racial Inequality
Using Ontological Modeling
Topic Statement
Submitted to Northcentral University
Graduate Faculty of the School of Business and Technology Management
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPY
by
Mark Crayton
Prescott Valley, Arizona
Feb 2011
2
SOLVING BLACK INEQUALITY USING ONTOLOGIES
3
Table of Contents
Proposed Topic ............................................................................................................................... 4
Introduction ..................................................................................................................................... 4
Project execution ......................................................................................................................... 5
Abridged Literature Review ........................................................................................................... 7
Problem Statement ........................................................................................................................ 10
Past studies ................................................................................................................................ 11
Solving the problem through research ...................................................................................... 12
Importance of research problem ............................................................................................... 13
Knowledge, skills and project time ........................................................................................... 13
Ontological technology ............................................................................................................. 13
Sufficient data ........................................................................................................................... 15
Purpose Statement ......................................................................................................................... 16
Explanation of approach ........................................................................................................... 17
Justification of purpose statement ............................................................................................. 18
Research Questions ....................................................................................................................... 19
Summary ....................................................................................................................................... 25
References ..................................................................................................................................... 27
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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Proposed Topic
This proposal is presented to support a Doctor of Philosophy with a concentration in the
area of Management Information System. This proposal will create a baseline for evaluation of
the Black American inequality in America. Using ontological model and semantic technology,
this study will describe Black American inequality measures and factors from an information
scientist’s viewpoint. The question for this study is, can an information scientist using
ontological modeling help solve the problem of Black American racial inequality in America?
Introduction
Social change in United States (U.S.) for Black Americans (or African Americans) could
not be more hopeful after the election of a black president in the 2008 election (Nagourney,
2008). So far, the facts are much different. Black unemployment in September 2000 was 7.3%, a
figure that was the lowest in ten years. During that same timeframe, unemployment for all
groups was comparatively, 3.9%. Six months later in March 2001 Black unemployment rate rose
to 8.3% while the overall rate inched up to 4.0% (BLS, 2011). In December 2010 a year after the
election of a Black president, Black unemployment was 15.8% while the overall percentage was
9.4%, and drilling down White and Hispanic/Latino unemployment was 8.5% and 13.0%
respectively.
In the area of U.S. education testing, in 2009 for grades 4, 8 and 12, the comparative
results for racial groups National Assessment of Educational Proficiency (NAEP) were:

Whites scored 163 for 4th grade, 162 for 8th grade and 159 for 12th grade

Blacks scored 127 for 4th grade, 126 for 8th grade and 125 for 12th grade

Hispanics scored 131 for 4th grade, 132 for 8th grade and 134 for 12th grade

Asian scored 160 for 4th grade, 160 for 8th grade and 164 for 12th grade
SOLVING BLACK INEQUALITY USING ONTOLOGIES

5
Native Americans scored 135 for 4th grade, 137 for 8th grade and 144 for 12th
grade.
The mean score for the NAEP is a 150. Black Americans score below every racial category in
the U.S., and this group’s score represents a significant level below the test average (NCES,
2011).
The problems for Blacks exist in a number of key places. Black (2007), gives a few
examples: “Black on Black” crime is virtually out of control, Black husbands and Black wives
have the highest divorce rate, Black males still have the highest unemployment rate and Black
youth under perform in education and 5 million Black males (of 40 million blacks) are now
actively involved in American criminal justice system (Black, 2007). Further evidence of Black
racial disparity is shown in recent studies of underachievement of Black youth compared to
Whites, Asian and Native American youth (Miranda et al., 2005). In a study, using cultural
intervention as a new approach, Black America youth closed the gap with other racial groups.
However, using generic cultural intervention techniques i.e. a normal school setting, no
appreciable impact was seen in the closure of the underachievement gap for this Black American
study groups. Adding parental management training to the study showed an impact on African
Americans and Latino youths in effectively showing scores that equaled White youths with
similar intervention. As a universal solution, these studies identify the problem and allude to a
solution; however, more scientific rigor is required to make an impact on the racial disparity
problem by a truly integrated final approach (Furstenberg, 2007).
Project execution
Psychologists, social scientists, anthropologist and doctors have studied the problem but
yet the Black American underachievement trend continues. An information scientist view point
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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may yield results that are trend changing. The basis for an information scientist view of solving
the problem comes from our current approach in the global war on terror (Dekorne &
Eisenhauer, 2002). The intelligence community for both military and civilian sectors use
semantic technology and the initial step of ontological modeling to catalog, track, resource and
determine program effectiveness in their war against global terrorism (Dekorne & Eisenhauer,
2002). Turning this type of technology towards the Black American problem may prove
invaluable for a nation struggling to compete. The application of a comprehensive analysis using
ontological methods to link exoteric facts may accomplishment a business focus and metrics
based model that begins to identify ways to change the Black American plight.
The approach for this project resides in the power of data linking that an ontological
model exhibits. In a project for the U.S. government, semantic web data using a set of
ontological models revealed a new IT spending profile from a business reference model (J.
Jackson, 2009). Using the ontological building blocks of objects-attributes-value (O-A-V)
triplets knowledge representation techniques can be used to represent new complex facts and
infer cognition (Gasevic, Djuric, & Devedzic, 2006). This proposal will leverage the power of
semantic technology using the framework that ontological modeling provides. Using the models,
a semantic inference can draw cognitive inference and new relationship resultants that describe
the Black American underachievement problem.
Changing the course of Black American underachievement, high incident of crime and
unequaled unemployment to name a few requires analysis that can find new relationships
between observed facts. This first study will establish the foundation for follow on research that
may include group intelligence, fuzzy logic and theoretical weighting algorithms (Jain & Martin,
1999). The key for this study is to explore and establish clear knowledge representation of the
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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Black American problem and clear relationships that may show inferences not considered by
other professional disciplines. With definitive new inferences, using semantic technology
through ontological modeling, hope may exist for solutions to Black American’s racial disparity
problem. Putting the foundation for Blacks, Whites, Asian and Native Americans on an equal
footing will help America remain prosperous and competitive internationally because less drag
will be on its government to provide for underachieving segments of its population
Abridged Literature Review
The abridged literature review provides a framework for establishing the importance of
the study and a benchmark for comparing results with other findings (Creswell, 2009). This
paper will frame the problem and note literature available to support the various theories,
questions, dependent variables, constraints and independent variables. As a mixed method study,
the abridged literature review will search for integrated approaches to Black Inequality problem.
For continuity, the topic and purpose statements follow as a review to highlight the fundamental
premise of this study.
The mixed method study requires the integration of sources that reveal concrete, abstract
and control data sets. The main approach of this paper will be to critically review reference
sources in relation to theories, questions and variables. The cited work will largely be less than 5
years; however, a large portion of this study is historical in nature and therefore dated references
form a significant part of determining independent variables.
There are three theories that apply to this study as noted in Figure 1 below. The first
theory the study will address is the fact that Black American inequality factors and measures
consist of 6 major items. This theory states that measuring the state of employment, education,
income, family togetherness, crime involvement and housing, for Black Americans compared
SOLVING BLACK INEQUALITY USING ONTOLOGIES
8
with other racial groups will yield a conclusion of how equal this racial group is with their
counterparts. The second theory is that counter-forces like criticism, which is a term used to
represent racial prejudice, racism, limiting opportunities based on race, or insensitivity are
factors in the overall effect of Black Inequality. Included in the counter-forces criticism would be
the effect of slavery and Jim Crow era events that have cultural and personality shaping impacts
in the Black community. The final theory is that behavioral sciences, specifically psychology and
sociology may actually hold independent variables that are causative factors to Black inequality
(Robbins, 2005).
Figure 1: A detailed review of Black Inequality initial casual factors
The role of anthropology is not diminished by this statement since a final model may determine
that anthropological factors have a heavier weight on the final inequality issue than first
expected. As an initial position, the study will target learning, motivation, training, and traditions
in the overall effect, which are primarily social science and psychology behavioral science
contributors.
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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The literature supporting each theory comes from sources in journals and books. There
are a plethora of activism studies; however, in short W.E.B. Dubois, Alfred Young and Deskins
Donald, provide a similar theme to other authors on the subject (Reed, 1997; Young & Donald,
2001). A collection of works in edited books support cultural variables, slavery and Jim Crow
impacts (W. D. Smith & Wells-Chunn, 1989; Smythe, 1976). Early sociological thoughts cover a
view of early Black sociologist evaluating various independent variables as noted in Figure 2.
Figure 2: Theory to Questions to Variable Mapping of the Black Inequality Model
Thoughts from these early science professional are reflected in racially active period of Black
awareness in the 1970’s and 1980’s where a group self evaluation across all variables was
occurring (Young & Donald, 2001).
The bulk of literature in support of the dependent and controlling variables in Figure 2 is
available from the US Census Bureau and Data.gov (Bureau, 2010; Gallagher, 2010). The
SOLVING BLACK INEQUALITY USING ONTOLOGIES
10
content on the sites are selectable to cover both historical and current statistics across a wide
range of government organizations and statistics bureaus. This straight line data from
government sources will be undisputable as an authoritative source and other references should
compare to the listed values within. Other sources of Black Statistical data supporting dependent,
control and some independent variables comes from the American Profile Series on Black
Statistics and the Journal of Blacks in Higher Education (InformationPublications, 2006; Slater,
2010).
Capturing authoritative sources for the independent variables is an area where the study
will need to validate and justify the findings. Many of the independent variables are subjective in
nature and various author conclusions may differ and shift the focus, quantitatively, when
attempting to model the abstracting in a relationship model. The Furstenberg (2007) article holds
promise in addressing culture related issues (Furstenberg, 2007). Wise (2010) provides a unique
perspective as a White American author who notes key findings about racism and quantitative
information on observed bias by Whites (Wise, 2010). Sykes (2008) focuses on housing related
issues and perceptions of Black American internal issues (Sykes, 2008). The technology on
ontology models is sufficient, current and detailed (Allemang & Hendler, 2008; Pahl & Holohan,
2009). Once the independent variables can be quantified in the form of a relationship or
comparative statement it can be modeled. Overall, the main point is for the study to carefully
document the independent variables so that scholars and businessmen can venerate the study and
promote the outcomes.
Problem Statement
Black Americans have not achieved racial equality in America in the areas of economics,
business, employment, competitiveness and family (Miranda, et al., 2005). Psychologists, social
SOLVING BLACK INEQUALITY USING ONTOLOGIES
11
scientists, anthropologists and medical comparative studies address problems and trends however
a comprehensive solution or segment focused papers have not shown national promise for Black
Americans. As a universal solution, these studies identified the problem but achievement of
Black racial parity requires further scientific rigor to make nationwide impact (Furstenberg,
2007). Black unemployment in September 2000 was 7.3%, a figure that was the lowest in ten
years. During this low point for Blacks comparatively, unemployment for all groups was 3.9%.
The problem of inequality continued in 2010 where Black American unemployment was 15.8%
while the overall unemployment percentage was 9.4%, and drilling down White and Hispanic
/Latino American unemployment was 8.5% and 13%, respectively. In the area of U.S. education
testing Black youths in grades 4, 8 and 12 under-achieved their White, Hispanic, Asian and
Native American counterparts in 2009 (NCES, 2011). Black (2007), states “Black on Black”
crime is virtually out of control, Black husbands and Black wives have the highest divorce rate,
and 5 million Black males (of 40 million Black Americans) are now actively involved in the
American criminal justice system (Black, 2007). Inequality unchecked may lead to political
instability because of the economic difference between one group in society and another group
(Bronner, 2011). Racial inequality illustrated across American’s history has been a catalyst for
unrest and as such provides a significant driver to justify going forward with this study (J. C.
Smith, 2003).
Past studies
African American studies can be traced back to starting with Frederick Douglass in the
1854 on racial determinism and significant work followed by W.E. B Du Bois in the 1915’s on
industrialization, collectivism and the new intellectuals (Reed, 1997). Until the 1960’s, White
archaeologists and anthropologists had not published any articles on the subject even though
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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slavery was first introduced in America of Virginia in 1619 (Blakey, 2001). Studies from Franz
Boas in the 1972 discussed cultural issues while Aufderheide and Rodreguez-Martin study on
demographics in 1998 opened additional approaches to the problem (Blakey, 2001). As Black
colleges flourished in the 1970’s, 1980’s and 1990’s studies of historical and modern day
inequality focused on discrete inequality areas of interest. As example, Smith and Wells-Chunn
(1989) republished a number of journal articles on education discussing equity and excellence in
the area of African American education however, much more needs to be done across the larger
scope of national Black American inequality (W. D. Smith & Wells-Chunn, 1989).
Solving the problem through research
Understanding the environment and cognitive issues influencing Black Americans gives
current and follow on research a chance to provide success factors, evaluate equality efforts and
coordinate mitigating strategies. Black American inequality represents individual decisions of
the nearly 40 million U.S. citizens (Bureau, 2010). Individuals and industry recognize the need to
improve Black American inequality gap while improving the country at large. For example,
Tabula Digiti’s produced a video game that has improved math scores 20% to 30% in 50 school
districts. Leshell Hatley received a grant for a Washington D.C. based Youth Application Lab
(YouthAppLab) to help African American children began to create iPhone applications (Talbert,
2011). Overarching research of Black inequality issues can expand upon the successes above by
generalizing the problem through a cognitive map set as a baseline to begin the process of a
comprehensive solution. If the scope of the study limits remains focused on a useful cognitive
map on new relationships and strategies, the previous project success in the use of semantic
technology may occur in this project (Gallagher, 2010; Leontidis, Halatsis, Grigoriadou, &
Vazouras, 2009).
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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Importance of research problem
This research problem may potentially be the catalyst for a regionally or nationally
sensitive roadmap for the success of a large majority of 40 million Americans who are
underachieving as a group in American society. Additionally, this study may garnish grant
money, government contracts and marketing dollars if it shows potential for increasing a
company’s market basis, lowers government social costs, expands the knowledge of racially
disparate employees or creates a stable cultural/political environment. Appling technology to an
old problem may allay calls from those continuing to demand compensation or reparation for
slavery, mid-1900 Jim Crowe laws of injustice, and discrimination in general (Torpey & Burkett,
2010).
Knowledge, skills and project time
Sufficient material, time and ability exist for this research to be performed. Semantic
technology is well known and additional training is available from Top Quadrant Semantic team.
The same team that executed the study of the Data.gov site and subsequent government
comparative study (Gallagher, 2010). Limiting the research study to foundational solutions and a
cognitive learning model for the initial phase of achieving parity gives hope for a successful
conclusion of the study in a reasonable period of time.
Ontological technology
It is appropriate to baseline the understanding of ontology modeling for the purposes of
going forward with the study. Ontology defines the basic terms and relations comprising the
vocabulary of the topic area as well as the rules for combining terms and relations to define
extensions to a new vocabulary. Ontologies are formal specification of a shared
conceptualization. The ontology may be of a variety of forms but in general it is a vocabulary of
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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terms and some specification of their meaning. The ontology includes definitions and an
indication of how concepts are inter-related, which collectively imposes a structure on the
domain being mapped and constrains of the possible interpretations of the terms and meaning
(Gomez-Perez, 2004).
Linking ontologies to universal modeling language (UML) terms requires understanding
of entity relationship and the expression of how these relationships between entities are noted.
An entity is a thing, person or object. A class is similar to an entity and is the basic unit of
ontology model allowing for an expression of the entities including the definition of relationships
between entity groups. Attributes expand the knowledge of a particular entity. Using an example
within a department store, the term shoe references to an object (or class) called shoe (footwear)
i.e. any shoe in the store. An attribute could be shoe color represented in modeling terms as
shoeColor. To further define a shoe class or entity other attributes can be assigned like
shoeGender, or shoeStyle. Going further the modeler can express a relationship between the
various entities. As such, adding to the example the modeler can add a class or entity called
location. Now the modeler can express a relationship between the entity of shoe and location.
The shoe is placedOn a location which could be a floor, shelf or bench. The location of where a
particular shoe is placed depends on the attributes of the location i.e. shelf, aisle, etc. For the
department store when these entities are fully mapped a model then exists where each shoe is
located and the store owner knows the relationship between each shoe and its location. In our
simple example the shoe was placed on the shelf but the shoe could also be hanging from the
shelf which drives another use of the model. In that case the model will drive a supply chain to
make sure there are enough hangers for shoes to hang on shelves in a particular location.
For the ontological modeling of Black American inequality, first this model has to
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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establish the vocabulary of the entities. An expansion of each entity via attributes builds out the
knowledge vocabulary of the various racial objects. Going further the modeler than determines
the relationships between the entities or classes and for this particular problem an ontological
vocabulary is available for use by an application. The power comes when two or more
vocabularies are combined to form another vocabulary that has its own properties and conditions
based on the relationships of original vocabularies and so on. This technology will form the basis
of the ontological mapping of the Black American inequality model.
Sufficient data
In the area of Black inequality, individual studies are numerous. Government and private
sources exist showing demographic break outs. Books and journal articles discuss education,
unemployment, and financial disparate statistics. The key will be limiting and removal of
nonrelated data sets. Additional semantic technology requires a representative model which is at
the heart of a useful cognitive solution. Nevertheless, exploring the solution and the crossfunctionality of a semantic model may lead to paring down of the information and unrelated data
by the very nature of the subject verb object ontology representations of qualitative and
quantitative facts on the racial demographic data.
The Black American inequality problem has a large scope from the perspective of a
research problem. Limiting this research to establishing an ontological baseline for explaining
the Black American inequity problem allows for the possibility that a cognitive model will serve
as a generalized approach to this multi-segmented problem. Evaluation of the semantic model
and its conclusions require significant level of effort to align the facts to a consistent ontological
approach. In solving the problem the cognitive model should converge however what if the
model diverges and the researched cognitive model shows Black American inequality is
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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unachievable (Black, 2007). That possibility creates an even greater challenge to a final study
dissertation defense.
Purpose Statement
The intent of this two-phase, sequential mixed methods study is to determine Black
American racial determinates that allow for use of ontological modeling and semantic
technology to determine equality models. The first phase will be a qualitative exploration of
authoritative attributes and entities that describe Black American issues. The data will consist of
social science, anthropology, psychology and medical observations. Participant data will cover
all major racial categories and represent observations on individual groups. Research data from
universities and corporate databases will provide the foundation for the qualitative study
(Creswell, 2009). Findings from the qualitative phase will then drive the tests, inferences and
semantic comparisons that will relate qualitative data. Data from World Wide Web registered
links of several case studies and use cases will provide a large pool of resource data.
Authoritative data from Data.gov, economic, financial, geographic and education combined in a
collaborative model set as dependent variables against independent variables of the qualitative
phase.
The reason for collecting qualitative data prior to a quantitative approach is that
demographic attributes must form some basis of fact in observed behavioral science for the
ontological model to be accepted. The ontological model and the follow on inference need an
initial foundation to compare results. Without an initial baseline of independent variables, the
model and any inference would need extensive testing to prove. The mixed approach is a means
tightening the scope of the study and coping with a large data set without exhaustive simulations
to eliminate erroneous initial ontological models.
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Explanation of approach
The method of using model driven architectures begins the ontology development and
allows for a concept model that addresses the true relationship between seemingly diverse racial
data sets (Gasevic, et al., 2006). Depending on the final application, the ontology enables Webbased knowledge processing, sharing and reuse between applications. By sharing common
concepts and the specialization of the concepts and vocabularies the model enables reuse across
multiple applications. The model is an abstraction of things in the real world, but is
simultaneously a thing of the real world. It provides a means to add meta-modeling concepts
both formally and informally. Proper modeling can be considered an art form because of the
informal nature of the sub-model concepts.
The coordination of facts describing real world events in a model allows for a new
ontology inference model from the base model. Unlike the standard unified modeling language
(UML), as noted in the ontological technology baseline section, an ontological model can work
with individuals, statements or objects within a model structure. This fact allows for the abstract
representation of individual facts as provided by a social science survey or objective facts from
government data on groups or organizations. Establishing relationships between real world
events, objects, and individuals leads to a new model learned from the previous set. An upper
level graph of the ontology provides and insight into the need to start with qualitative data and
then add the quantitative data in support of the rest of the model.
The visualization of a high level model as noted in Figure 3 shows physical entities that
represent objects and processes combined with abstract entities formulated in sets, classes,
relations to include propositions, quantity and additional attributes. As discuss, the ontological
model can then introduce factual universal data from additional databases and other web data
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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resources. In the third portion of the model architecture relationships between various data,
abstractions, universal facts formulate additional exists to give the model additional semantics.
Figure 3: Top Level Concept of the Upper Level Ontology as an example of data representation.
Justification of purpose statement
Use of a mixed modeling study of qualitative and quantitative methods is driven by the
real possibility that the learning ontology forms collaboration of a new class of information not
previously observed. Use of the ontology model to describe the African American problem
originates in the complex nature between objects, individual and relationships and the need to
assimilate the data. The driving force behind first using qualitative methods, emanates from the
real concern that inconsistencies may exist with new inference classes and relationships.
Expounding on the inconsistencies of ontologies, there are two ways to deal with
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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inconsistency in the ontologies. The first method is to fix each occurrence as encountered. For
small models this represents a useful method for the architect. The second method to use requires
a defensible argumentation on the front end of the ontology architecture to minimize
inconsistency of the sub-model groups. Use of real world attributes from social scientists,
anthropologist, economist, psychologists and universal data sets is the method to minimize errant
data relationships.
The purpose of the new ontology model is a description of the Black American inequality
in a manner not previously observed by individual studies. The new model relationships
contained in XML technologies, specifically resource descriptive framework (RDF) and web
ontology language (OWL) eases further processing of a new ontology model and the possible
application of comparative data sets as an adjunct to the new concepts and conclusions. The
resulting model would save time to any downstream development and allow for the extension of
the conclusions experienced during this study (Gallagher, 2010).
Research Questions
The research questions and hypotheses combined in a question bank are broken out by
behavioral, technical and general areas. Creswell (2009) recommends a combined question bank
when using a mixed method study to support the writer’s intent to integrate or connect between
the quantitative and qualitative phases of the study (Creswell, 2009). Since the study is highly
dependent on the integration of the qualitative and quantitative questions, an integrated approach
is appropriate. The major question of each major subcategory group is indicated as an APA
heading level 3. Subsequent lower level questions shall seriate and provide an explanation in
body style text.
Q1. What factors are necessary for Black American’s to claim racial equality.
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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The question requires the study to determine and adjudicate variables of success or
failure of Black American equality. The follow-on questions must express the support of some
empirical evidence and independent variables as measures.
Q2. Is there still an impact of slavery, servitude, Jim Crowe laws or lack of civil rights?
The impact of historical factors may contribute to modern day events. Cultural and
historical force effects may linger across years of history and need some consideration of
whether to include in the ontological model. Although difficult because of the lack of early
comprehensive data, correlating and relating current observed facts to past facts might prove an
important step in identifying causes of Black American inequality.
Q3. What criteria will be used to determine if observed behavioral science facts are
independent, dependent or a control variable?
Using ontological methods require independent, dependent and control variables existing
with a range of values necessary to support expression of subject-verb-object (S-V-O) statement.
Because of the nature of the resource descriptive framework (RDF) model, it may be more
important to graph only collaborated data so as to minimize inconsistent ontological
representations in the final inference model (Gomez, Chesnevar, & Guillermo, 2010). The other
methodology concern is accounting for multiple RDF expressions that require graphing the facts
in both S-V-O directions. The combinations of these multiple inferences allow the establishment
of new facts. For example, given a discussion of the state of Black American men, is it criminal
activity in the community or the poor economic state of that community that causes poor
performance in schools. Would the independent variable be the criminal activity level or the poor
family economic situation that impacts the dependent variable called school performance? Using
resource descriptive framework, I would set the S-V-O notation for both independent and
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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dependent variable sets in a bi-direction web ontology language relationship and determine from
other facts which one is a downstream force in the model of Black Inequality. Control variables
of age brackets add additional breakouts. This is a critical question to the modeling effort since
behavioral science data may be expressed in correlation statistic and not in actual exact
numbered sets.
Q4. Will modernity be the guiding principle of how much, when and what data will be
modeled to give authenticity to the new ontological representations?
Modernity is a concept that sociologists have applied to a wide range of social conditions
and circumstances in the post-Enlightenment era (after the 1930’s) which includes exploration of
the proliferation of bureaucracy and formal institutions designed to help regulate social life,
urbanization, capitalism, industrialization and other social developments associated with these
occurrences and transformations (Young & Donald, 2001). Limiting the input data set will help
reduce the scope of the study. Also, it will eliminate the need of adding data from an era where
formal Black sociological scientific discipline did not exist. In making this distinction, historical
information tagged to geographical locations or to individuals is a fair representation of the facts.
For example, the fact that Mississippi, Alabama and Georgia were slave states and fought on the
side of the Confederacy is a fact. This statement of fact is not a behavioral science issue and
therefore does not falls under the criteria of modernity. If post-enlightened observations are not
available then those facts would not be relevant to the study as it relates to the model.
Q5. What are the critical breakouts of the behavioral science variables?
The behavioral areas selected in the initial breakout of variables are education, income,
labor, family, sports and housing. There is no hard and fast rules from sociologist; however,
Jackson (1986), gives a number of interesting facts that support this break out of behavioral
SOLVING BLACK INEQUALITY USING ONTOLOGIES
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variables (E. M. Jackson, 1986). The breakout proposed is education, housing, employment,
income, crime, family (i.e. divorce, kids, single family etc) and location. These breakout
categories form the traditional independent variables; however, additional variables may need to
be included. Drug usage, health rates, video games usage, teacher quality, gangs, morality,
religion and extracurricular activities to name a few may have significant impact into the
independent variables for this study.
Q6. Is it important from a behavioral science perspective that the model needs to show
convergence of various inference facts as a criterion of success?
The convergence of the ontological model is a desired end point for the study (Gomez, et
al., 2010). Inconsistency may invalidate the final model and its usefulness to other researchers
who may want to use the findings of this study. Inconsistencies cause serious concerns for
potential users especially if the inconsistencies are random or unpredictable. Trusting an
ontology model is as important as the information being conveyed. Rework of each and every
inconsistency shown in the final semantic execution is a necessity, especially since some aspects
of this discussion carry emotional responses by individuals and groups.
Q7. Can Black inequality factors be modeled to obtain useful information.
This question and the subordinate questions drive at the capability of the ontological
model to actual solve the problem being proposed for the study. This is a critical
necessity of the study and an important going forward position.
Q8. Can resource descriptive framework (RDF) and web ontology language (OWL)
capture abstract facts of data, information and knowledge of a cognitive nature?
Cognitive science has the view that mental states and processes actually mediate between
input stimuli and output responses. The science suggests that information in the mind is encoded
SOLVING BLACK INEQUALITY USING ONTOLOGIES
23
into chunks, mental procedures that allow for encoding and decoding of information (Gasevic, et
al., 2006). Commonly used types of human knowledge are procedural, declarative,
metaknowledge, heuristic, structural, inexact and uncertain, commonsense and ontological.
Procedural knowledge is about how to do something. Declarative knowledge describes what is
known about a topic or a problem. Metaknowledge is knowledge about knowledge. It is used to
decide what other knowledge is best suited for solving the current problem. Heuristic knowledge
includes rules and guidelines that help the problem-solving process. Heuristics is not strict;
however there is basis for use of past experience to interpret the future knowledge solution.
Structural knowledge describes mental models and the organization of problems and solutions in
a certain space.
The type of knowledge called inexact and uncertain, characterizes problems, topics and
situations in which information is imprecise, unavailable incomplete, random and ambiguous.
This kind of knowledge representation carries the greatest risk for this project. Commonsense
knowledge has a root in commonly held human knowledge that contains no precise theories or
format. Finally, ontological knowledge represents knowledge with a certain domain construct. It
is knowledge that describes the terms necessary to express the items within the domain.
Understanding the types of knowledge will aid in the representation of the knowledge facts and
figures (Gasevic, et al., 2006).
Q9. What criteria will be used to determine if the model is complete?
The process for constructing the ontology will be first to analyze the input sources and to
develop baseline taxonomy. The next step is consultation with experts and expert sources to
develop a baseline authoritative taxonomy. The addition of relations and axioms complete a
refinement process. The last step will be determining the opportunity areas and identifying the
SOLVING BLACK INEQUALITY USING ONTOLOGIES
24
most promising focus areas and target solution for the ontology. The end goal is the expression
of the model across the behavioral science success factors (Gomez-Perez, Fernandez-Lopez, &
Corcho, 2004).
Q10. What are the tools necessary to complete this project?
The anticipated software products for this project include TopBraid Composer and
Ensemble from Top Quadrant, Microsoft Visio, and Altova Enterprise MissionKit 2011 Suite.
Top Braid Composer and Ensemble provide a powerful set of tools for managing the model and
the semantic engine for executing the final ontological inference model once the initial
taxonomy, universal variables, control variables and relationships are added (TopQuadrant,
2011). Microsoft Visio provides a powerful graphics tool to construct visual graphs, flowcharts
and presentations. Lastly, use of the Altova Enterprise MissionKit 2011 Suite give a complete
integrated development environment of tools from the UML diagrams, schemas, RDF, and OWL
integration (Altova, 2011). These are the main tools for modeling the ontology models. Likely
separate database software like Microsoft SQL or Microsoft Access depending on the size of the
triplet stores is needed to complete the model technical implementation.
Q11. What is the usefulness of the Black Inequality ontological model?
On the World Wide Web there are a number of unique ontology models on a number of
subjects (Baker, Heath, Noy, Swick, & Herman, 2010). There are vocabularies or models for
radiological procedures, automotive repair, portal service for academic research, drug ontology
project for Elesevier (Netherlands) and digital music archive (DMA) for Norwegian National
Broadcaster. With the wide range of linked models on the W3C.org (World Wide Web
Symposium international organization) today, a Black American Inequality ontology model
would be a candidate for linking and possibly publishing to a W3C organization or similar.
SOLVING BLACK INEQUALITY USING ONTOLOGIES
25
Q12. Who would use a Black Inequality ontological model?
All research to date shows that a combined RDF/OWL model for Black Inequality does
not exist. It is a good assumption that the current producers of statistics may be candidates to
consume the data and information results. A significant user is the U.S. Government and Military
especially the minority affairs offices (Gallagher, 2010). Accurate information would be useful
in the comparison of diversity statistics and best use of minority recruiting assets. Other
anticipated users are commercial marketing organizations that consume demographic
information for a targeted select group of product owners. Accurate demographic data could be
sold as part of a service to those users.
Summary
Changing the course of Black American underachievement, high incident of crime and
unequaled unemployment to name a few requires analysis that can find new relationships
between observed facts. This first study will establish the vocabulary for follow on research that
may include input from group intelligence, fuzzy logic and theoretical weighting algorithms
(Jain & Martin, 1999). The key for this initial study is to explore and establish clear knowledge
representation of the Black American problem and clear relationships that may show inferences
not considered by other professional disciplines. With new vocabularies and inferences drawn
from the mapping of discrete and abstract Black American’s racial disparity problem, maybe a
clear linkage between independent and dependent may occur.
The impact of older controlling variables may be the most difficult to map. Early
sociologist study with discrete data was difficult to obtain and further, it is not necessarily
verifiable and quantitative in nature. Nevertheless, the power of ontological modeling and the
subsequent running on an inference engine allows for mapping of the imprecise information and
SOLVING BLACK INEQUALITY USING ONTOLOGIES
26
then reviewing the model to determine the inconsistencies of the final vocabulary. Additional
iterations and model revisions during research and design phases can allow convergence of this
unrelated data into a meaningful end product.
The technology is well documented and a number of helpful sources both in print and
knowledgeable scholars provide a confidence that both the initial and final vocabularies are
possible given authoritative data is available for mapping. The artful nature of this study will be
the abstract data representation and selecting the right data relationships to use in the model.
Exploration of statistical surveys and regression data to narrow abstract facts into a meaning
entity-attribute-relationship (formally noted above as S-V-O) is imperative to moving this study
forward. The review from experts in the field of social science and psychology of the final
product can ensure the final technological product makes sense to observed real world events.
If the question of this study is answered, then the business community would have a
powerful tool to build upon to justify racially targeted programs, grants and initiatives. Clear
relationships between the expenditure and return on investments may occur. Like the work in the
intelligence community noted above, this study can provide a powerful baseline in which other
applications can build upon. The work from this study can also be published with the W3C
international organization in its many linked ontologies library for business and academia users.
Follow on research to expand the applications into prediction and leading metrics are follow-on
projects to add additional worth to this study.
SOLVING BLACK INEQUALITY USING ONTOLOGIES
27
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