Visual knowledge representation of conceptual semantic networks

Knowledge visualization (KV) and knowledge representation (KR) are distinguished, though both are knowledge management processes. Knowledge visualization is subject to humans, whereas knowledge representation – to computers. In computing, knowledge representation leverages reasoning of software agents. Thus, KR is a branch of artificial intelligence. The subject matter of KR is representation methods. They are classified into (1) knowledge level and symbol level representations; (2) procedural and declarative representations; (3) logic-based, rule-based, frame- or object-based representations (supporting inference by inheritance); and (4) semantic networks. In legal informatics, methods of legal knowledge representation (LKR) are dealt with. An essential feature of LKR is the representation of deep knowledge, which is mainly tacit. It is easily understood by professional jurists and hardly by amateurs from outside law. This knowledge comprises the teleology of law and a whole implicit framework of legal system. The paper focuses on (1) identifying key features of KV and KR in the legal domain; and (2) distinguishing between visualization, symbolization, formalisation and mind mapping.

Download Full-text

Configurable Product Structure Model Based on Semantic Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.2074 ◽

2013 ◽

Vol 655-657 ◽

pp. 2074-2079

Author(s):

Xin Wang ◽

Lin Gao ◽

Chong Chong Ji

Keyword(s):

Knowledge Representation ◽

Structural Model ◽

Semantic Network ◽

Semantic Networks ◽

Product Structure ◽

Semantic Relation ◽

Product Configuration ◽

Configuration Design ◽

Structure Model ◽

Representation Form

Depending on the demand of structure model in product configuration design, product types that can be configured are described and analyzed. Based on semantic networks as a kind of available knowledge representation form and Extend A/O tree, structural model of configurable product is put forward. The structural relation, assembly relation and configuring option relation are included, semantic relation among assembly parts is also expressed. Finaly, configurable node model is proposed.

Download Full-text

SEMANTIC NETWORK ARCHITECTURES: AN EVALUATION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213092000132 ◽

1992 ◽

Vol 01 (01) ◽

pp. 57-83

Author(s):

JOSE G. DELGADO-FRIAS ◽

STAMATIS VASSILIADIS ◽

JAMSHID GOSHTASBI

Keyword(s):

Artificial Intelligence ◽

Knowledge Representation ◽

Semantic Network ◽

Evaluation Criteria ◽

Semantic Networks ◽

Computer Architectures ◽

Network Architectures ◽

Network Processing

Semantic networks as a means for knowledge representation and manipulation are used in many artificial intelligence applications. A number of computer architectures, that have been reported for semantic network processing, are presented in this paper. A novel set of evaluation criteria for such semantic network architectures has been developed. Semantic network processing as well as architectural issues are considered in such evaluation criteria. A study of how the reported architectures meet the requirements of each criterion is presented. This set of evaluation criteria is useful for future designs of machines for semantic networks because of its comprehensive range of issues on semantic networks and architectures.

Download Full-text

KNOWLEDGE REPRESENTATION SCHEME BASED ON PETRI NET THEORY

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001488000431 ◽

1988 ◽

Vol 02 (04) ◽

pp. 691-700 ◽

Cited By ~ 17

Author(s):

SLOBODAN RIBARIĆ

Keyword(s):

Knowledge Representation ◽

Petri Net ◽

Semantic Networks ◽

Formal Description ◽

Inference Procedure ◽

Representation Scheme

An original knowledge representation scheme named KRP based on Petri net theory is proposed. The formal description of the scheme, and the inference procedure similar to "intersection search" in semantic networks, are given.

Download Full-text

Knowledge-Based Expert System Development and Validation with Petri Nets

Journal of Information & Knowledge Management ◽

10.1142/s0219649208001907 ◽

2008 ◽

Vol 07 (01) ◽

pp. 37-46 ◽

Cited By ~ 3

Author(s):

Madjid Tavana

Keyword(s):

Expert System ◽

Knowledge Representation ◽

Petri Nets ◽

Decision Trees ◽

System Development ◽

Semantic Networks ◽

Knowledge Based ◽

Medical Diagnostic ◽

High Level ◽

Decision Tables

Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

Download Full-text

TermFrame: A Systematic Approach to Karst Terminology

Dela ◽

10.4312/dela.54.149-167 ◽

2021 ◽

pp. 149-167

Author(s):

Špela Vintar ◽

Uroš Stepišnik

Keyword(s):

Knowledge Representation ◽

Domain Knowledge ◽

Systematic Approach ◽

Interdisciplinary Approach ◽

Domain Model ◽

Knowledge Network ◽

Data Driven ◽

Knowledge Representations ◽

Visual Knowledge ◽

Data Driven Approach

We describe a systematic and data-driven approach to karst terminology where knowledge from different textual sources is structured into a comprehensive multilingual knowledge representation. The approach is based on a domain model which is constructed in line with the frame-based approach to terminology and the analytical geomorphological method of describing karst phenomena. The domain model serves as a basis for annotating definitions and aggregating the information obtained from different definitions into a knowledge network. We provide examples of visual knowledge representations and demonstrate the advantages of a systematic and interdisciplinary approach to domain knowledge.

Download Full-text

Ontologies and Medical Terminologies

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch225 ◽

2011 ◽

pp. 1463-1469

Author(s):

James Geller

Keyword(s):

Knowledge Representation ◽

Computer Science ◽

World Wide ◽

Semantic Networks ◽

Systematic Account ◽

Area Of Interest ◽

Representation Systems ◽

Classical Paper ◽

Interchange Format ◽

Frame Systems

The term “Ontology” was popularized in Computer Science by Thomas Gruber at the Stanford Knowledge Systems Lab (KSL). Gruber’s highly influential papers defined an ontology as “an explicit specification of a conceptualization.” (Gruber, 1992; Gruber 1993). Gruber cited a conceptualization as being “the objects and concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them.” (Genesereth & Nilsson, 1987). The term “Ontology” has been used in computer science at least since (Neches, 1991), but is derived from philosophy where it defines a “systematic account of existence,” usually contrasted with “Epistemology.” Gruber’s work is firmly grounded in Knowledge Representation and Artificial Intelligence research going back to McCarthy and Hayes classical paper (McCarthy & Hayes, 1969). Gruber’s work also builds on frame systems (Minsky, 1975; Fikes and Kehler, 1985) which have their roots in Semantic Networks, pioneered by (Quillian, 1968) and popularized through the successful and widespread KL-ONE family (Brachman & Schmolze, 1985). One can argue that Gruber’s ontologies are structurally very close to previous work in frame-based knowledge representation systems. However, Gruber focused on the notion of knowledge sharing which was a popular topic at KSL around the same time, especially in the form of the Knowledge Interchange Format (KIF) (Genesereth, 1991). Ontologies have recently moved center stage in Computer Science as they are a major ingredient of the Semantic Web (Berners-Lee et al., 2001), the next generation of the World-Wide Web. Ontologies have also been used in Data Mining (see below) and in (database) schema integration.

Download Full-text