Using Organizational Semiotics and Conceptual Graphs in a Two-Step Method for Knowledge Management Process Improvement Measurement

2010 ◽  
pp. 427-442
Author(s):  
Jeffrey A. Schiffel
Keyword(s):  
Natural Language ◽  
Process Improvement ◽  
Knowledge Engineering ◽  
Conceptual Graphs ◽  
Step Method ◽  
Dataflow Graphs ◽  
Process Ontology ◽  
Step Process ◽  
Patterns Of Behavior ◽  
Organizational Semiotics

The semantic normal forms of organizational semiotics extract structures from natural language texts that may be stored electronically. In themselves, the SNFs are only canonic descriptions of the patterns of behavior observed in a culture. Conceptual graphs and dataflow graphs, their dynamic variety, provide means to reason over propositions in first order logics. Conceptual graphs, however, do not of themselves capture the ontological entities needed for such reasoning. The culture of an organization contains natural language entities that can be extracted for use in knowledge representation and reasoning. Together in a rigorous, two-step process, ontology charting from organizational semiotics and dataflow graphs from knowledge engineering provide a means to extract entities of interest from a subject domain such as the culture of organizations and then to represent these entities in formal logic reasoning. This paper presents this process, and concludes with an example of how process improvement in an IT organization may be measured in this two-step process.

Organizational Semiotics Complements Knowledge Management

2011 ◽  
pp. 104-122
Author(s):  
Jeffrey A. Schiffel
Keyword(s):  
Information System ◽  
Natural Language ◽  
Knowledge Engineering ◽  
Conceptual Graphs ◽  
Dataflow Graphs ◽  
Process Ontology ◽  
Step Process ◽  
Patterns Of Behavior ◽  
It Organization ◽  
Organizational Semiotics

Inserting the human element into an Information System leads to interpreting the Information System as an information field. Organizational semiotics provides a means to analyze this alternate interpretation. The semantic normal forms of organizational semiotics extract structures from natural language texts that may be stored electronically. In themselves, the SNFs are only canonic descriptions of the patterns of behavior observed in a culture. Conceptual graphs and dataflow graphs, their dynamic variety, provide means to reason over propositions in first order logics. Conceptual graphs, however, do not of themselves capture the ontological entities needed for such reasoning. The culture of an organization contains natural language entities that can be extracted for use in knowledge representation and reasoning. Together in a rigorous, two-step process, ontology charting from organizational semiotics and dataflow graphs from knowledge engineering provide a means to extract entities of interest from a subject domain such as the culture of organizations and then to represent these entities in formal logic reasoning. This chapter presents this process, and concludes with an example of how process improvement in an IT organization may be measured in this two-step process.

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 Single Step Process for Crystalline Ni-B Compounds

Materials ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1259 ◽  
Author(s):  
Mahboobeh Shahbazi ◽  
Henrietta Cathey ◽  
Natalia Danilova ◽  
Ian Mackinnon
Keyword(s):  
Single Phase ◽  
Single Step ◽  
Transformation Mechanism ◽  
Solid Reaction ◽  
Step Method ◽  
Step Process ◽  
Pressure Time ◽  
Autogenous Pressure ◽  
Higher Temperature ◽  
High Yields

Crystalline Ni2B, Ni3B, and Ni4B3 are synthesized by a single-step method using autogenous pressure from the reaction of NaBH4 and Ni precursors. The effect of reaction temperature, pressure, time, and starting materials on the composition of synthesized products, particle morphologies, and magnetic properties is demonstrated. High yields of Ni2B (>98%) are achieved at 2.3–3.4 MPa and ~670 °C over five hours. Crystalline Ni3B or Ni4B3 form in conjunction with Ni2B at higher temperature or higher autogenous pressure in proportions influenced by the ratios of initial reactants. For the same starting ratios of reactants, a longer reaction time or higher pressure shifts equilibria to lower yields of Ni2B. Using this approach, yields of ~88% Ni4B3 (single phase orthorhombic) and ~72% Ni3B are obtained for conditions 1.9 MPa < Pmax < 4.9 MPa and 670 °C < Tmax < 725 °C. Gas-solid reaction is the dominant transformation mechanism that results in formation of Ni2B at lower temperatures than conventional solid-state methods.

scholarly journals From Existential Graphs to Conceptual Graphs

2014 ◽  
pp. 439-472
Author(s):  
John F. Sowa
Keyword(s):  
Natural Language ◽  
Computational Linguistics ◽  
Formal Semantics ◽  
Semantic Networks ◽  
Conceptual Graphs ◽  
Computationally Efficient ◽  
Graphic Notation ◽  
Neural Processes ◽  
Existential Graphs ◽  
The Mind

Existential graphs (EGs) are a simple, readable, and expressive graphic notation for logic. Conceptual graphs (CGs) combine a logical foundation based on EGs with features of the semantic networks used in artificial intelligence and computational linguistics. CG design principles address logical, linguistic, and cognitive requirements: a formal semantics defined by the ISO standard for Common Logic; the flexibility to support the expressiveness, context dependencies, and metalevel commentary of natural language; and cognitively realistic operations for reasoning by induction, deduction, abduction, and analogy. To accommodate the vagueness and ambiguities of natural language, informal heuristics can supplement the formal semantics. With sufficient background knowledge and a clarifying dialog, informal graphs can be refined to any degree of precision. Peirce claimed that the rules for reasoning with EGs generate “a moving picture of the action of the mind in thought.” Some philosophers and psychologists agree: Peirce's diagrams and rules are a good candidate for a natural logic that reflects the neural processes that support thought and language. They are psychologically realistic and computationally efficient.

A KNOWLEDGE-BASED CHINESE LETTER-WRITER

1988 ◽  
Vol 02 (01) ◽  
pp. 37-48
Author(s):  
KOH TOH TZU
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Engineering ◽  
Systems Science ◽  
Form Processing ◽  
Design And Implementation ◽  
Knowledge Based ◽  
Chinese Business ◽  
Letter Writer

Since the end of last year, the researchers at the Institute of Systems Science (ISS) started to consider a more ambitious project as part of its multilingual programming objective. This project examines the domain of Chinese Business Letter Writing. With the problem defined as generating Chinese letters to meet business needs, investigations suggest an intersection of 3 possible approaches: knowledge engineering, form processing and natural language processing. This paper attempts to report some of the findings and document the design and implementation issues that have arisen and been tackled as prototyping work progresses.

scholarly journals Intelligent Retrieval for Biodiversity

2016 ◽  
Vol 25 (01) ◽  
pp. 1550029
Author(s):  
M. Vilares Ferro ◽  
M. Fernández Gavilanes ◽  
A. Blanco González ◽  
C. Gómez-Rodríguez
Keyword(s):  
Mathematical Model ◽  
Natural Language ◽  
Information Management ◽  
Language Processing ◽  
Domain Knowledge ◽  
Automatic Generation ◽  
Conceptual Graphs ◽  
Computational Tools ◽  
Start Up ◽  
Natural Language Interaction

A proposal for intelligent retrieval in the biodiversity domain is described. It applies natural language processing to integrate linguistic and domain knowledge in a mathematical model for information management, formalizing the notion of semantic similarity in different degrees. The goal is to provide computational tools to identify, extract and relate not only data but also scientific notions, even if the information available to start the process is not complete. The use of conceptual graphs as a basis for interpretation makes it possible to avoid the use of classic ontologies, whose start-up requires costly generation and maintenance protocols and also unnecessarily overload the accessing task for inexpert users. We exploit the automatic generation of these structures from raw texts through graphical and natural language interaction, at the same time providing a solid logical and linguistic foundation to sustain the curation of databases.

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