Temporal Activity Flow as a Form of Ontological
Real-World Knowledge Data Mining
VG JOHNSON
Graduate School of Computer and Information Sciences
Nova Southeastern University
3301 College Avenue, Fort Lauderdale, Florida 33314
UNITED STATES
Abstract: - This research represents work that introduces the notion of an activity embedded into an autonomic
inference ontological system of real world knowledge. It is not enough for an ontology to recognize the fact
that "network traffic is high". For an ontological system to be of increasingly important use, it must also (for
example) recognize that "high network traffic" is a state that exists within a "Denial of Service Attack"
activity. The system must be able to understand and identify the previous states that must have occurred in
order to reach the state of "high network traffic": hence the notion of the activity. Because activities cannot
exist without the existence of some type of time component, time as a characteristic of activity flow needs to
be accounted for. This understanding adds another level of capability to an ontological system. This work
explores the theory and implementation of incorporating a concept called Temporal Activity Flow (TAF)
directly into the design of a modern, real-world knowledge, commonsense, ontology creating an Autonomic
Inferencer capable of handling inference processing as related to real-world activities, concepts, objects, and
events.
Key-Words: - Autonomic Inference, Ontology, Activity, Artificial Intelligence, Natural Language Processing
1 Introduction
Recent advances in hardware and software systems
have encouraged the work being done in the area of
natural language (NL), commonsense, and real
world knowledge ontologies. However, the merging
of natural language corpora with large commonsense
systems has not yet led to breakthroughs, of a
significant magnitude, in autonomic inference. In
order to achieve this, the concept of an activity,
something that is not prominently infused in modern
commonsense ontological systems, needs to be
accounted for in order to support inference
processing. It is not enough for a NL-based ontology
to recognize the fact that "network traffic is high".
For a NL-based ontology to be of increasingly
important use, it must also recognize that "high
network traffic" is a state that exists within a "Denial
of Service Attack" activity. The NL-based ontology
must be able to understand and identify the previous
states that must have occurred in order to reach the
state of "high network traffic": hence the notion of
the activity. This richness adds another level of
understanding to the system. Consequently, in order
to provide for that type of performance, a separate
application would be devised that would incorporate
facilities to handle this type of deductive processing.
This means that many domain-dependent
applications would need to be written to use the NLontology as a back-end component.
This research intends to introduce a method of
natural language and commonsense understanding
that embeds the notion of an activity into an
ontology’s core. The atomic terms, assertions,
and/or axioms found within modern commonsense
ontologies are typically constructed distinctly
different: with each element composed of unique
data structures. The goal is to create an Autonomic
Inferencer (AI) that incorporates the concepts
relating to activities directly within an NL-based
ontology of real world, commonsense knowledge
thereby eliminating the need for domain-dependent
driver applications. Doing so would allow for the
terms, assertions, and axioms to be constructed and
referred to using identical means: thereby allowing
the system a low-level understanding of its internal
processes. Thus, all of the elements, though different
in composition and function, are processed
identically. Being domain-independent in nature,
this research has bearing on multiple disciplines and
fields. The data-mining possibilities will present
opportunities for the discovery of new knowledge.
This would be made possible by introspection of the
activities making up various elements of real world
knowledge.
2 Problem Formulation
Data mining approaches have been explored for
many years in the artificial intelligence field. Many
different algorithms have been devised from neural
networks to systems that utilize statistical methods.
Large databases contain data that has been
accumulated over a long span of time provide
optimal environments from which to process mining
activities. However, the emergence of ontologies has
provided another source for data mining activities.
While many ontologies are domain-specific, a few
ontologies are domain-independent and filled with
real-world knowledge. These “commonsense”
ontologies, as they are commonly called, are
emerging to provide a unique alternative for the
mining of real-world knowledge.
2.1 Ontologies
Researchers have begun to employ ontologies [5] to
assist in solving complicated tasks requiring large
knowledge-bases. The use of commonsense
ontologies enables researchers to be able to
incorporate a body of knowledge that would be
required for processing every day facts (such as
understanding that a chair is meant to be sat on).
While there are expert systems designed specifically
for the purposes of annotating, managing, and
processing facts, ontologies can be less focused, in a
sense broader, in that they are constructed somewhat
differently. In order for an expert system to be
created, one must bring together the skill sets of a
Knowledge engineer and a Domain expert. The
Domain expert is any person or group of persons
possessing the skills and knowledge that are going to
be necessary to replicate within the expert system.
For example a particular expert could have a broad
range and/or depth of understanding of medical
terminology, procedures, and skills related to
pediatric medicine. In order to capture the expert’s
knowledge within an expert system, the use of the
Knowledge engineer is also required.
The
Knowledge Engineer performs the tasks necessary to
create the expert system. They will also perform the
extraction of Knowledge (through questioning) from
the Domain expert for the purposes constructing a
deductive tree within the expert system. The result
would be an expert system that is capable of
answering questions that could have been posed to
the Domain expert directly. The use of deduction
within the expert system is its primary means of
deriving solutions for queries posed to it.
While conceptually and physically different, an
ontology can be built using some of the same
procedures used in creating expert systems. There
are systems such as Cyc (a rather large collection of
common sense axioms and real world knowledge
that has been developed over the past two decades
by Dr. Lenat [12] and his team of scientists,
linguists, and engineers) that is used to solve some
of the issues involving commonsense and its
application to artificial intelligence. He believes that
in order to make the strides necessary to create an
artificially intelligent system, there needs to be a
basic set of understanding that the intelligence
would need to have. Cyc was created with this
purpose in mind. Composed of dozens of highly
trained individuals, the Cyc team manually input
millions of rules (consuming tens of millions of
dollars) into the system. These rules took on form of
statements such as “ a chair is for sitting” and “
people drive cars”. Their aim is to have the system
able to be incorporated as a component within larger
systems to provide for the basic element of inference
logic such that the system would not need to perform
highly expensive computing tasks simply to identify
the purpose of the chair within the context of some
statement. The belief is that if a system were to have
that basic information, then it will already have a
base set of knowledge: a limited intellect. Thus,
this particular ontology has the characteristics for
being a broad-based application. This is similar to
the way in which the WordNet ontology [13] has
been created for assisting in the processing of natural
language. Both ontologies are broad-based and
capable of being incorporated into the research of
scientists across many domains. This has the desired
effect of facilitating large-scale reuse thereby
increasing the efficiency and validity of the research
performed given that the reusable components of the
researched systems have already gone through
validation and verification. However, even when
using Cyc, there is missing information inside of a
commonsense knowledge-base. This missing
information relating to activities needs to be
captured in order to understand, in terms of context,
some of the issues that will potentially surface
during the processing of natural language.
Another real world knowledge ontology that is
similar in form to Cyc is the ThoughtTreasure
ontology [14]. This ontology, which was used for
the purposes of processing natural language, was
created in a much shorter time when compared to the
development resource consumption of the Cyc and
WordNet ontologies. The design of this particular
ontology is based upon modules and agents that are
specific to a particular aspect of knowledge. The
ontology covers many of the aspects that need to be
accounted for within the processing of artificially
intelligent systems. While imbued with a usable
portion of common sense knowledge, like Cyc, it
lacks the same basic components of information
needed for understanding general knowledge.
In order to understand the purpose of this
research, one should not confuse the notion of an
activity for that of context. Context, as it pertains to
the knowledge contained within modern ontologies
and NLP systems, relates to the point of view that a
term can take when encountered within a dialogue.
Accordingly, the function of the term “can” can take
on different meanings depending on whether it is
being used as a helping verb or a noun within the
context of a sentence. Conversely, an activity, as
defined for the purposes of this research, is a term
representing a concept that can be broken down into
a series of tasks and/or other activities performed in
time. Accordingly, it is possible for an activity to be
affected by context. It is also possible for an activity
to dictate context. This occurs because an activity is
a special type of term that is composed of the
interactions of many different terms. Thus, during
the course of an activity, context can shift and morph
into different states depending on the terms involved
and the actions performed on them.
Given the previous information, one might begin
to question how a TAF-based ontology might differ
from the plan or activity ontology as described by
Tate [15]. In that particular work, an ontology was
created in order to support the concepts related to the
making and carrying out of plans and for the
purposes of process interchange. Tate’s definition of
a plan is similar, conceptually, to what an activity
represents in TAF. Temporal constraints containing
attributes for the beginning and ending of activities
and identifying intervals and duration were specified
in order to accommodate the concepts inside of the
plan ontology. Likewise, the same general ideas can
be seen in work on the TOVE ontology [11].
However definitive the activity/plan ontologies
might seem, their approaches only address activities
from the perspective of something that can be
observed, experienced, or otherwise manipulated by
the axioms and assertions found within the ontology.
Conversely, in a TAF-based ontology the very
foundation of the ontology is based in TAF where
the notion of an activity resides at the process
structure (what would normally be programming
code for performing operations on data). It also
resides at the metadata (what would be data used to
define the form of the data that is manipulated by the
process structure) and data-storage (the actual stored
data) level of the TAF-based ontology. In this way,
the TAF elements are subject to CRUDS (Create,
Read, Update, Delete, Store) operations by other
process-typed TAF components. Thus, the result is
an ontology that is in itself a large, nested group of
activities encapsulated within a singular activity.
The ontology, itself, becomes a TAF.
2.2 Natural Language Approaches
Research in the field of NLP has yielded insights
into how to parse, correctly and more accurately,
natural language text. Taking for example one such
system, LOLITA [8] emerged as a general-purpose
natural language processor during the middle
1990’s. While shown to be considerably accurate
during different test evaluations [8], this system still
lacked a certain knowledge that is normally used
when interpreting text in order to evaluate the
correctness of one word over another. Thus, even
after decades of development on LOLITA, certain
abilities could be seen lacking. WordNet is another
example of the modern approaches used. A recurring
issue that emerges with ontologies like WordNet is
that while it will provide adequate information for
natural language researchers, it does not, like
LOLITA, contain some of the more basic
information that might be needed in order to disambiguify some of the words that may occur
regularly when speaking a natural language [13].
While some ontologies are good at providing the
lexical information needed for natural language
processing, the commonsense knowledge that goes
into contextual analysis of natural language is
missing. In those instances, ontologies such as Cyc
can either be brought to bear on the problem at hand
as a separate system or the multiple ontologies could
be merged in some way. However, that creates an
environment consisting of multiple applications
again.
While contextual analysis and ambiguity are two
areas of concern within the realm of natural
language, these types of discourse issues have been
addressed to varying degrees of success by various
natural language approaches [2]. Unfortunately, the
high accuracy rates of the systems in resolving
context and ambiguity have been tied to
performance penalties [2]. Thus, even if adequate
ontological sources exist to aid in proficient natural
language processing, overall system performance
will then become the obstacle to overcome. In
related work, Ali, Channarukul, and McRoy [1]
identify and explore some current natural language
systems. During the course of their work, they
identify that the method of “planning” the output of
language tailored to specific applications can
become a burden on management and complexity.
Thus, they explore a number of systems that
generate real-time natural language discourse.
Through their work, four main types of text
generation are identified:
1. Canned text is akin to error messages and
informational blurbs that are created at
design-time of an application.
2. Template-based generation uses templates
that are pre-filled with information from the
user or the conversation. Templates can take
the form of sentences, paragraphs, or similar
natural language structures with key nouns,
pronouns, and verb-forms missing. The
missing fields are then filled from
information supplied by the user or inferred
by the system. This method is good for realtime performance.
3. Phrase-based generation is a collection of
phrases (noun phrase, verb phrase,
prepositional phrase, etcetera) that are used
like building blocks to create output
sentences. Similar to the templates, the
phrases will be filled with appropriate words
matching the lexical requirements of the
phrase. The difficulty of managing this
method is directly proportional to the size of
the grammar.
4. Feature-based
generation
involves
identifying every attribute within a grammar
as a feature. Real-time performance will
suffer, as the grammar must be traversed
during generation of text, thus limiting the
size of the grammar. However, the quality of
the generated text is the best of the four
approaches.
Additionally, they present a system aptly called Yet
Another Generator that generates natural language
discourse. While they focus on language generation,
the four features can be applied to the processing of
natural language input. Perhaps, there are
alternatives
to
the
considerable
resource
requirements and length of time that it took to bring
a system such as Cyc into reality. While Cyc was
constructed manually using CycL (an augmented
implementation of first-order calculus), humans
learn, quite proficiently, through direct manipulation
of natural language.
2.3 Process Tracking using Flowcharts
Flow charts, in the past and present, have been used
to convey the overall design of systems and ideas
whether the systems are represented as mental
thoughts, processes, or even the flow of
programming code. This research intends to expand
upon some of the concepts of flowcharting and
standardize the way in which the flow is created,
interpreted, and understood. While flow-charting
may be taught in many educational institutions
during the early parts of computer educational
training [7], it has since ceased being a strong
element with focus within disparate communities.
This is due in part to some of the major
shortcomings that are readily apparent when
attempting to use flowcharting fundamentals to
describe modern-day design: which can be objectoriented in nature. Many different toolsets have
emerged to take the place of flowcharting that would
more accurately depict modern-day systems.
Nevertheless, this research focuses on the simplicity
with which the flow-charting concept can explain
process fundamentals. Thus, the most applicable
research for the purposes of research is quite dated
with respect with other research that represents the
more modern tools. The dated research on ANSI
flowcharting [9] and some of the newer research on
Uniform Modeling Language (UML), of which the
Activity diagram correlates most with flowcharting
[16], have been explored within the context of this
work.
3 Problem Solution
The human intellect is a nested structure of
directions, events, personality, and object memories.
All of the preceding memory elements are subject to
some type of activity. Mathematics, language, and
sports skills are readily recognizable as being
affected by activity. Even the formation of abstract
thoughts can have at its foundation activity. While
activity is at the core of so much in reality, there are
very few computer systems that place activity at the
very core of artificial intelligence processing.
3.1 Cause and Effect
The concept of cause and effect permeates the
existence of everything: even within one’s deepest
thoughts. This would include processes and/or
actions that are known, objects that are in some way
experienced in three dimensions, and even concepts
that are abstract in nature such as color and
emotional expression. Cause and effect has its place
in written/verbal instructions and in the creation of
three-dimensional objects. These objects can be
food-based (by using a menu) or it can be an
assembly-required Christmas toy (by using provided
instructions). Additionally, cause and effect can be
experienced when building objects that may seem
abstract in nature. A common example of this
occurs in the computer world when building objectoriented applications. For example, a Customer
object can consist of multiple attributes such as
name, address, telephone number, and multiple
methods that can act upon the object. These objects
are abstract in nature in that they do not exist in the
3D plane, but rather as collections of bits within the
memory of a computer system. In order for the
application to run, the Customer object would need
to be loaded by some type of object management
module. This module would in turn assemble the
object from its specification by adding multiple
strings to represent the attributes that the class will
have.
This process can likewise be partially
explained by the concepts of cause and effect by
stating that because the attributes are present, the
object has the ability to persist state. However,
cause and effect cannot exist solely on its own
without the influence of another major element.
3.1
Temporal Characteristics
Without the existence of temporal characteristics,
the concepts of cause and effect could not be
possible. There is an order in the way cause and
effect affects operations. That order is most often
sequential in nature with the effect being temporally
dependent upon the cause. Without this type of
framework in place, the relationship between cause
and the effect of the new venture could not be
accurately described. Given that this framework is
in place and the multitude of events that exists in
reality, one could infer that almost every instance of
the effect of some event can serve as a possible
cause in another. This can be seen in a cascading
automobile accident. While the entire event can be
thought of as being a single occurrence in time,
multiple events can be present within the occurrence
that can in fact be chained. Take for example
multiple rear end collisions. The first car could have
applied its brakes causing the second car to impact
into its rear end which would then cause a following
car to impact into the rear of the second car and so
on until the events of the cascading car accident
comes to a conclusion.
One can see the
dependencies within the car accident and identify
the chained effects that cause and effect imposes on
events. Therefore, the concept of cause and effect
needs to be explained in a way that standard flow
charts are incapable of explaining. Temporal
Activity Flow addresses this need.
3.2 TAF Defined
The number of activities that can make up the
tapestry of one’s life is immeasurable. However, it
is the innate knowledge or understanding of the
concepts surrounding an activity that must be
captured inside of a real world knowledge ontology
in order to facilitate intelligence. Instructions or
directions are clear examples of activities. The
activity occurs in time and thus it can be thought of
as having temporal qualities (they must be
performed in some type of order). The order can be
sequential, concurrent, or even looped. Because
tasks are state-based, an activity, which can be
composed of multiple tasks and/or activities, is also
state-based. Consequently, the ideas surrounding
“cause and effect” are likewise affected by the
understanding of TAF. The term “cause” represents
an activity. The term “effect” represents the state of
being of an object that occurs after the performance
of an activity. The diagram, as found in Fig. 1,
illustrates the concepts of the flow of a temporal
activity. On initially glancing at the diagram, one
should almost immediately recognize the TAF as
being similar to basic flowcharting [9] or to the
more recent Uniform Modeling Language Activity
diagram [16]. Flowcharting captures the basics of
idea flow and is similar in form to TAF. The
symbols inside of a TAF diagram are similar to the
symbols found in flowcharting as well. Thus, one
would find decision symbols represented by
diamonds, process symbols represented by
rectangles, ovals that represent some type of control
state within the diagram, and connectors represented
by a circle. Additionally the TAF diagram will also
have a rectangle with rounded corners as found in
some flowcharting extensions.
While a TAF
diagram may be similar to a flow chart in form, its
uniqueness is found in the rules that control the way
in which the symbols are placed within a TAF
diagram. The diagram itself does not represent a
novel creation. It is the most direct and easiest to
comprehend way to represent the ideas and concepts
which must be incorporated into a real world
knowledge, natural language processing-based,
ontological system.
Figure 1: TAF Defined
Indeed, the diagram as a simple flowchart does not
represent a novel creation in its viewable form. The
rules encapsulated within a TAF diagram, the colors
used for its various elements, and the time attributes
infused within all contribute to its novelty. While a
TAF can be described graphically, it must, in its true
form, be conceptualized inside of an ontological
system. The concepts that make up a TAF must be
understand by the system in order to process the
individual actions and the flow of states within an
activity. The diagram illustrates a flow of states
within the activity. The constant flow from one state
to another constitutes action within a TAF. A system
would need to understand the concepts of the flow of
actions/events that could happen concurrently with
the flow of other events. It would need to understand
the concepts that the flow of one event/activity could
lead to a state that represented a previous state. This
would constitute a loop in the activity. While this
may be simplistic for a human to understand, an
Autonomic Inferencer would need to understand the
basic concepts involved with a simple loop in order
for it comprehend that it is possible to move
backwards within a flow of activity. Modern
programming languages do not satisfy this need
from the point of view of the machine understanding
what is occurring. Instructions written in modern
programming languages only control a machine. The
machine need not know or even understand what the
instructions are dictating. In fact, unless specifically
designed to do so, machines are not even able to
anticipate the effect of a program on another
seemingly unrelated program. TAF is a way of
describing the concepts that are constrained by rules
in a way that process and data flow diagrams are not.
Traditional process and data flow diagrams can
display a virtually unlimited form of flows in that
the symbols used to describe the process in those
diagrams can be placed anywhere within the canvas
regardless of the location of other symbols (for the
most part). A TAF diagram represents a process that
has stringent guidelines on the location and
placement of individual states within a flow of
activity. It is because of these rules coupled with
other temporal characteristics of an activity that the
Autonomic Inferencer could be capable of logicbased operations. With a collection of nested TAF’s
and a suitable inference engine in place, the system
will be able to perform inference operations on
individual TAF’s as a result of the existence of other
TAF’s. This evaluation of cause and effect will
allow the inference engine to approximate future
effects. The accumulation of TAF’s will allow for
pattern analysis to take place: given that the TAF’s
will have a conceptualized form.
4 Conclusion
The potential benefits of having an intelligent
machine that is based on TAF are manifold. This
study will aim to produce a system of that is capable
of autonomic inference (inference of new knowledge
through introspection and external stimuli). This is a
task performed by humans, for the most part, in
every moment of their lives. It is done so well and so
soundly that most people do not even realize that the
thoughts that they are having can be explained using
the concepts of TAF. The decisions that they make
could likewise be explained using those very same
concepts. Analysis as to why certain events occur
or why some statements are factual is an operation
related to inference. The human intellect is capable
of autonomic inference of real world knowledge in a
highly granular and multitasked fashion. Given that
the information that humans process is real world
knowledge, the concepts that are within that domain
of knowledge are widespread. Everything that a
person knows, encounters, and in most cases, ever
dreams about is based on concepts, objects, and
events that fall within the domain of real-world
knowledge. It is important to understand that the
concepts of TAF belie the understanding that
humans have about the world at large.
TAF has at its foundation characteristics of time.
Consequently, TAF could not exist were it not for
the temporal mechanics framework that it relies on.
Time is a measurement for a change in state while
TAF is a collection of concepts and rules that
describe actions and activities that alter states.
These actions and activities are nothing more than
changes in state: hence, their reliance on time as a
component. This research will attempt to embed
these concepts into the many terms and concepts
making up the domain of real world knowledge
within the ontology. This will create a system that is
capable of understanding actions and activities as
they relate to the concepts, objects, and events
within the domain of real-world knowledge. This
will give the intelligent system an innate
understanding of the very fundamentals of those
elements of real world knowledge. Humans have
this innate ability as well. Humans are capable of
looking at an object and decomposing it into various
components and subsystems. They would know
how four sticks standing upright positioned in a
rectangle with a flat plane atop them forms a table of
some kind. This is something that a human can do
because of their immersion into the existence society
thrives in. Research has been performed with other
systems that are currently in research to embed some
type of commonsense knowledge within intelligent
systems. However, those approaches have taken a
hand-held, manually input approach to the
development of such systems. Their approach has
taken decades to explain to an intelligent system
what those researchers consider as commonsense
knowledge about the real world concepts. Those
systems have a understanding of reality as it was
defined to them. Unlike those systems that require a
large body of highly skilled and trained individuals
to construct those large commonsense ontologies,
the system that is being developed through this study
will derive the same elements of commonsense
regarding real world concepts through the analysis
and inference of those real-world concepts using
TAF. It will then be capable of learning from its
experiences because the very act of learning is
indeed an activity and that activity can be explained
using TAF.
This study is limited in that the development of
the intelligent system will only be focused on the
acquisition of real world knowledge within an
ontology based on the concepts of TAF. Natural
language concepts will be used to assist in
ontological formation and inference activities. While
TAF allows the system to be capable of complex
natural language understanding and generation, the
development of such TAFs is outside of the scope if
this study. Consequently, the natural language
abilities that will be present within the system will
suffice only for the purposes of knowledge
acquisition and template-based language generation.
An intelligent system as described by this study does
not currently exist. The system designed for this
study is based on the concepts of TAF. From the
point of view of this study, the human mental
process relies upon the concepts of TAF for
everyday performance. The intelligent system that
will be in a complete state at the end of this study
will serve as a foundation for future work. The
system’s primary goal will be identifying hidden
relationships amongst real-world objects, concepts,
and events: data mining of reality without the need
for specific algorithms, databases, or software.
Given the infinite number of possibilities that exist
in that domain, the advancement of knowledge, from
the point of view of the intelligent system, would be
perpetual. This will be made possible only through
the intelligent system’s foundation in and conceptual
understanding of Temporal Activity Flow.
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