Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks

TL;DR

This paper introduces a method combining Bayesian analysis and Gallois lattices to optimize fuzzy semantic networks, enabling the system to learn and refine word relationships through user interactions and contextual inference.

Contribution

It presents a novel approach integrating Bayesian analysis with Gallois lattices for adaptive learning and optimization of fuzzy semantic networks.

Findings

Improved semantic network representation through iterative learning.

Effective filtering of network relationships using Bayesian analysis.

Enhanced understanding of natural language words in fuzzy networks.

Abstract

This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.