Representation of a Sentence using a Polar Fuzzy Neutrosophic Semantic Net
This work addresses the challenge of encoding polar semantics for machines, potentially enabling emotion representation in robots, but it appears incremental as it builds on existing semantic nets and neutrosophy concepts.
The paper tackles the problem of representing sentence semantics with polarity (positive, neutral, negative) by extending a conventional semantic net into a Polar Fuzzy Neutrosophic Semantic Net using neutrosophy, and demonstrates its implementation in MATLAB for English sentences to enable machine emotion representation.
A semantic net can be used to represent a sentence. A sentence in a language contains semantics which are polar in nature, that is, semantics which are positive, neutral and negative. Neutrosophy is a relatively new field of science which can be used to mathematically represent triads of concepts. These triads include truth, indeterminacy and falsehood, and so also positivity, neutrality and negativity. Thus a conventional semantic net has been extended in this paper using neutrosophy into a Polar Fuzzy Neutrosophic Semantic Net. A Polar Fuzzy Neutrosophic Semantic Net has been implemented in MATLAB and has been used to illustrate a polar sentence in English language. The paper demonstrates a method for the representation of polarity in a computers memory. Thus, polar concepts can be applied to imbibe a machine such as a robot, with emotions, making machine emotion representation possible.