A Context Centric Model for building a Knowledge advantage Machine Based on Personal Ontology Patterns

Throughout the industrial era societal advancement could be attributed in large part to introduction a plethora of electromechanical machines all of which exploited a key concept known as Mechanical Advantage. In the post-industrial era exploitation of knowledge is emerging as the key enabler for societal advancement. With the advent of the Internet and the Web, while there is no dearth of knowledge, what is lacking is an efficient and practical mechanism for organizing knowledge and presenting it in a comprehensible form appropriate for every context. This is the fundamental problem addressed by my dissertation.;We begin by proposing a novel architecture for creating a Knowledge Advantage Machine (KaM), one which enables a knowledge worker to bring to bear a larger amount of knowledge to solve a problem in a shorter time. This is analogous to an electromechanical machine that enables an industrial worker to bring to bear a large amount of power to perform a task thus improving worker productivity. This work is based on the premise that while a universal KaM is beyond the realm of possibility, a KaM specific to a particular type of knowledge worker is realizable because of the limited scope of his/her personal ontology used to organize all relevant knowledge objects.;The proposed architecture is based on a society of intelligent agents which collaboratively discover, markup, and organize relevant knowledge objects into a semantic knowledge network on a continuing basis. This in-turn is exploited by another agent known as the Context Agent which determines the current context of the knowledge worker and makes available in a suitable form the relevant portion of the semantic network. In this dissertation we demonstrate the viability and extensibility of this architecture by building a prototype KaM for one type of knowledge worker such as a professor

A Work-Pattern Centric Approach to Building a Personal Knowledge Advantage Machine

A work pattern, also known as a usage pattern, can be broadly defined as the methods by which a user typically utilizes a particular system. Data mining has been applied to web usage patterns for a variety of purposes. This thesis presents a framework by which data mining techniques could be used to extract patterns from an individual\u27s work flow data in order facilitate a new type of architecture known as a knowledge advantage machine. This knowledge advantage machine is a type of semantic desktop and semantic web application that would assist people in constructing their own personal knowledge networks, as well as sharing that information in an efficient manner with colleagues using the same system. A knowledge advantage machine would be capable of automatically discovering new knowledge which is relevant to the user\u27s personal ontology.;Through experimentation, we demonstrate that a user\u27s file usage patterns can be utilized by software in order to automatically and seamlessly learn what is important as defined by the user. Further research is necessary to apply this principle to a more realized knowledge advantage machine such that decisions can be fueled by work patterns as well as semantic or contextual information