Intelligence Semantics

Download Conceptual Graphs and Fuzzy Logic: A Fusion for Representing by Tru Hoang Cao PDF

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By Tru Hoang Cao

The ability for people to speak utilizing language permits us to offer, obtain, and comprehend info expressed inside a wealthy and versatile representational framework. additionally, we will be able to cause according to traditional language expressions, and make judgements in accordance with the data they communicate, even though this data frequently consists of obscure phrases and unsure evidence. particularly, conceptual graphs invented by means of John Sowa and fuzzy good judgment based by way of Lofti Zadeh have the typical goal of representing and reasoning with linguistic details. At this juncture, conceptual graphs offer a syntactic constitution for a tender mapping to and from traditional language, whereas fuzzy good judgment presents a semantic processor for approximate reasoning with phrases hav-ing imprecise meanings. This quantity is the mixed results of an interdisciplinary study programme fascinated with the combination of conceptual graphs and fuzzy common sense for varied wisdom and data processing projects that contains average language. First, it truly is approximately fuzzy conceptual graphs and their common sense programming foundations, as a graph-based order-sorted fuzzy set good judgment programming language for automatic reasoning with fuzzy item attributes and kinds. moment, it extends conceptual graphs with basic quantifiers and develops direct reasoning operations on those prolonged conceptual graphs, which may be mapped to and from ordinarily quantified usual language statements. 3rd, it defines similarity and subsumption measures among item kinds, names, and attributes and makes use of them for approximate retrieval of data represented in graphs. eventually, it proposes a strong ontology-based process for figuring out traditional language queries utilizing nested conceptual graphs.

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Additional info for Conceptual Graphs and Fuzzy Logic: A Fusion for Representing and Reasoning with Linguistic Information

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Some ground atoms can be mapped to inconsistent values to express local inconsistencies of those atoms only. As such, an annotated logic program with inconsistent clauses still has models and does not arbitrarily entail everything. Whereas, in classical logic programming, no inconsistency is allowed in a model, whence a program with inconsistent clauses has no model and thus everything becomes its logical consequence. Annotated logic programming appears to be a good framework for fuzzy set logic programming, where fuzzy set values are treated as annotations.

E is a set of edges, each of which connects a relation vertex to a concept vertex. The number of edges incident to a relation r is arity(type(r)) and the edges are labelled from 1 to arity(type(r)). For every i from 1 to arity(type(r)), the concept connected to r by the edge labelled i is denoted by neighbour(r, i). , arity(type(r))}, arg_type(type(r), i) ≤ι type(neighbour(r, i)), where each t∈TC in arg_type(type(r), i) is considered as a special fuzzy concept type (t, absolutely true). 1 is (PERSON, ENTITY), then the inclusion of PERSON in the concept [{PERSON, (AMERICAN_MAN, very true)}: John] is to make the FCG well-typed.

The mismatching degree of one fuzzy type to another is then formally defined as follows, where T and F are respectively the TRUE-characteristic and the FALSEcharacteristic complete sub-lattices of a fuzzy truth-value lattice of discourse. 7. Let τ1 = (t1, v1) and τ2 = (t2, v2) be two fuzzy types such that τ1 is matchable to τ2. Then the mismatching degree of τ1 to τ2, denoted by Δ(τ1 | τ2), is defined to be either: 1. Δ(v1 | v2) if t1 = t2, or 2. Δ(v1 | lub{v∈T | v ≤ι v2}) if t1 <ι t2 and ∃v∈T: v ≤ι v2, or 3.

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