722,047 research outputs found
KOMBASE - a knowledge representation system with frames for an object-oriented knowledge base
Knowledge representation is an important area of research which is currently being done in the field of Artificial Intelligence (AI). In order to manipulate the wealth of information available in a typical AI application, mechanisms must be provided to represent and to reason with knowledge at a high level of abstraction. Knowledge representation with frames is a structured and object-oriented approach to this problem. KOMBASE is a prototype to a frame-based system containing organizational information of companies and other corporate bodies. This paper describes the approach adopted in the development of KOMBASE and discusses its implementation, particularly from a knowledge representational perspective
Integration of object-oriented knowledge representation with the CLIPS rule based system
The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
A knowledge-based machine vision system for space station automation
A simple knowledge-based approach to the recognition of objects in man-made scenes is being developed. Specifically, the system under development is a proposed enhancement to a robot arm for use in the space station laboratory module. The system will take a request from a user to find a specific object, and locate that object by using its camera input and information from a knowledge base describing the scene layout and attributes of the object types included in the scene. In order to use realistic test images in developing the system, researchers are using photographs of actual NASA simulator panels, which provide similar types of scenes to those expected in the space station environment. Figure 1 shows one of these photographs. In traditional approaches to image analysis, the image is transformed step by step into a symbolic representation of the scene. Often the first steps of the transformation are done without any reference to knowledge of the scene or objects. Segmentation of an image into regions generally produces a counterintuitive result in which regions do not correspond to objects in the image. After segmentation, a merging procedure attempts to group regions into meaningful units that will more nearly correspond to objects. Here, researchers avoid segmenting the image as a whole, and instead use a knowledge-directed approach to locate objects in the scene. The knowledge-based approach to scene analysis is described and the categories of knowledge used in the system are discussed
DESIGNING OBJECT-ORIENTED REPRESENTATIONS FOR REASONING FROM FIRST-PRINCIPLES
Modeling expert knowledge using "situation-action" rules is not always feasible in knowledge
intensive domains involving volatile knowledge (e.g., trading). The explosive search
space involved in such domains and its dynamic nature make it extremely difficult to setup
a rule base and keep it accurate. An alternative approach suggests that in some domains
many of the rules expert use can be derived by reasoning from "first-principles". That approach
entails modeling experts' deep knowledge, and emulating reasoning processes with
deep knowledge that allow experts to derive many of the rules they use and justify them.
This paper discusses the design and implementation of an object-oriented representation
for the deep knowledge traders utilize in a business domain called hedging, which is knowledge
intensive and involves volatile knowledge. It illustrates how deep knowledge modeled
using that representation is used to support reasoning from first-principles. The paper also
analyzes features of that representation that we have found to be extremely beneficial in
the development of a knowledge-based system called INTELLIGENT-HEDGER. Based on
our experience we feel that, with minor modifications, this representation can be used in
other managerial domains involving financial reasoning.Information Systems Working Papers Serie
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