From actantial model to conceptual graph: Thematized action in John Cage's 0′00′(4′33′′No. 2)

2020 ◽  
Vol 14 (3) ◽  
pp. 307-328
Author(s):  
Michael D. Fowler
Keyword(s):  
Conceptual Graph

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

scholarly journals Active Semantic Relations in Layered Enterprise Architecture Development

2021 ◽  
pp. 3-16
Author(s):  
Matt Baxter ◽  
Simon Polovina ◽  
Wim Laurier ◽  
Mark von Rosing
Keyword(s):  
Directed Graph ◽  
Enterprise Architecture ◽  
Directed Graphs ◽  
Building Blocks ◽  
Semantic Relations ◽  
Formal Concept ◽  
General Applicability ◽  
Conceptual Graph ◽  
Architecture Development

AbstractEnterprise Architecture (EA) metamodels align an organisation’s business, information and technology resources so that these assets best meet the organisation’s purpose. The Layered EA Development (LEAD) Ontology enhances EA practices by a metamodel with layered metaobjects as its building blocks interconnected by semantic relations. Each metaobject connects to another metaobject by two semantic relations in opposing directions, thus highlighting how each metaobject views other metaobjects from its perspective. While the resulting two directed graphs reveal all the multiple pathways in the metamodel, more desirable would be to have one directed graph that focusses on the dependencies in the pathways. Towards this aim, using CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) and a LEAD case study, we determine an algorithm that elicits the active as opposed to the passive semantic relations between the metaobjects resulting in one directed graph metamodel. We also identified the general applicability of our algorithm to any metamodel that consists of triples of objects with active and passive relations.

scholarly journals OBJECT-BASED KNOWLEDGE REPRESENTATION OF FEMALE RELATED ISSUES FROM THE HOLY QURAN

2014 ◽  
Author(s):  
Ku Ruhana Ku-Mahamud ◽  
Aniza Mohamed Din ◽  
Noraziah Che Pa ◽  
Faudziah Ahmad ◽  
Wan Hussain Wan Ishak ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Semantic Network ◽  
Diffi Cult ◽  
Object Based ◽  
Holy Quran ◽  
Conceptual Graph ◽  
The Holy Quran ◽  
Systematic Structure

Focusing on the use of Semantic Network and Conceptual Graph (CG) representations, this paper presents an easy way in understanding concepts discussed in the Holy Quran. Quran is known as the main source of knowledge and has been a major source reference for all types of problems. However understanding the issues and the solution from the Quran is diffi cult due to lack of understanding of Quran literature. Meticulously, the Quran contains much important information related to female. However, this information are scattered and complexly linked. Technically, to extract and present the encapsulated knowledge on female matters in the Quran is a challenging task. Thus, this paper discusses on how to understand and represent the knowledge in an easy way. A total of 18 female terms are identifi ed. Through the terms, the name of surah, verses number and text from the verses are gathered. The texts are then analyzed and clustered into specifi c issues. Result of the analysis that consists of extracted knowledge on female issues is presented in a systematic structure using Semantic Network and CG. The strength and advantages of both approaches are compared, discussed and presented.  

Cognitive Task Analysis Using Complementary Elicitation Methods

1995 ◽  
Vol 39 (9) ◽  
pp. 525-529 ◽  
Author(s):  
Sallie E. Gordon
Keyword(s):  
Cognitive Complexity ◽  
Task Analysis ◽  
Cognitive Task ◽  
Cognitive Task Analysis ◽  
Graph Analysis ◽  
Conceptual Graphs ◽  
Different Types ◽  
Conceptual Graph ◽  
High Degree ◽  
Domain Independent

Cognitive task analysis is accomplished using a wide variety of methodologies, and we have previously argued that different methods will tend to elicit qualitatively different types of knowledge and skills. Because of this, many practitioners use complementary methods for a given project. We have developed such a complementary package of knowledge elicitation techniques, along with a specific representational method, which together are termed conceptual graph analysis. Conceptual graph analysis is domain-independent and can be used to evaluate complex cognitive tasks or subtasks. It relies on the successive use of document analysis, interviews, task observation, and induction based on review of task performance. The information from these elicitation techniques is represented as a set of interrelated conceptual graphs, but can be represented in other formats also. There are several issues relevant to cognitive task analysis that are currently being faced, including when to perform this type of analysis, and what methods to use. One answer is to perform cognitive task analysis when the task has an inherently high degree of cognitive complexity.

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