Ontology-Based Understanding of Natural Language Queries Using Nested Conceptual Graphs

2010 ◽  
pp. 70-83 ◽  
Author(s):  
Tru H. Cao ◽  
Anh H. Mai
Keyword(s):  
Natural Language ◽  
Conceptual Graphs

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 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.

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.

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.

Representing Clinical Narratives Using Conceptual Graphs

1995 ◽  
Vol 34 (01/02) ◽  
pp. 176-186 ◽  
Author(s):  
R. H. Baud ◽  
A. M. Rassinoux ◽  
J. C. Wagner ◽  
C. Lovis ◽  
C. Juge ◽  
...  
Keyword(s):  
Natural Language ◽  
Declarative Knowledge ◽  
Conceptual Graphs ◽  
Human Reasoning ◽  
Language Understanding ◽  
Complete Representation ◽  
Medical Narratives ◽  
Adequate Representation ◽  
Representation Language ◽  
Semantic Compatibility

Abstract:The analysis of medical narratives and the generation of natural language expressions are strongly dependent on the existence of an adequate representation language. Such a language has to be expressive enough in order to handle the complexity of human reasoning in the domain. Sowa’s Conceptual Graphs (CG) are an answer, and this paper presents a multilingual implementation, using French, English and German. Current developments demonstrate the feasibility of an approach to natural Language Understanding where semantic aspects are dominant, in contrast, to syntax driven methods. The basic idea is to aggregate blocks of words according to semantic compatibility rules, following a method called Proximity Processing. The CG representation is gradually built, starting from single words in a semantic lexicon, to finally give a complete representation of the sentence under the form of a single CG. The process is dependent on specific rules of the medical domain, and for this reason is largely controlled by the declarative knowledge of the medical Linguistic Knowlege Base.

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.

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