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

Abstract

A knowledgeable person, in old times, has been regarded as a saint who knows everything without stepping out of house. Current technologies provide us with the ability of acquiring knowledge as one saint did before. Today, overloading of information always results in the quest for efficiently taking possession of useful information. With the advent of pervasive computing, information gathering is happening in real time. It has become possible that a collaborative knowledge network is constructed by utilizing little resource, but accurate under a carefully established environment. In this paper, we propose a new architecture, knowledge advantage machine (KaM), that helps people to construct a knowledge network. And we would like to apply a term, "call it once", into a novel type of knowledge discovery method. A typical User of KaM machine covers only one particular domain. The Domain information consists of a user defined ontology, a set of user collection of knowledge unit, we called them JANs, and some necessary tools to facilitate the knowledge discovery process. The "call it once" model transforms the traditional knowledge discovery process to a few basic simple steps (three steps).

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

“…In 2011 Luyi Wang et al [40] published a context-centric approach to creating a knowledge advantage machine. This paper provides a basis for the problem; a KAM can be succinctly defined as any device which increases the efficiency at which a knowledge worker performs daily tasks.…”

“…Here, we also introduce the concept of a "JAN," or atom of knowledge, described by Wang et al. [40] as an "abstract object for all the general resources". The idea of a JAN is not entirely new, and, indeed, has existed under different names in the past; one might liken a JAN to a "knol" from Google's eponymous Knol project [2].…”

“…This data can then be used to identify contexts and predict future file usage. The difference between these two approaches is simple, but crucial: both are attempting to gain a knowledge advantage, but the former concentrates on using domain information [40] to do so, and the second focuses on user-specific work-patterns.…”

“…A KAM would utilize a semantic link network [45] in order to better organize and utilize information for a given knowledge worker. Such a system would probably require some initial work on the user's part [40] to set up, but the goal would be that the resulting "knowledge advantage" would counteract this effort.…”

“…A semantic link network, in its semantic organization of information for knowledge workers, would allow knowledge workers to perform their jobs with added ease and efficiency [40]. It follows logically that any properly designed Knowledge Advantage Machine would, in theory, facilitate the semantic link network in a manner which is more comfortable for knowledge workers to use than the traditional methods of information gathering and application.…”