Optimization of Fuzzy Semantic Networks Based on Galois Lattice and Bayesian Formalism
This work addresses the challenge of enhancing natural language interpretation in user-specific semantic networks, though it appears incremental as it builds on existing methods like Galois lattice and Bayesian analysis.
The paper tackles the problem of optimizing fuzzy semantic networks by combining Galois lattice and Bayesian analysis to learn new words from user queries and improve network representation, resulting in a simplified and more efficient semantic network through inductive filtering.
This paper presents a method of optimization, based on both Bayesian Analysis technical and Galois Lattice of Fuzzy Semantic Network. The technical System we use learns 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 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 Galois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.