Fuzzy Semantic Model Research Articles

Personalized fuzzy semantic model of PHFLTS: Application to linguistic group decision making

The proportional hesitant fuzzy linguistic term set (PHFLTS) has been effectively employed in analyzing the group’s hesitancy in linguistic group decision making (LGDM). The application of PHFLTS assists in capturing the individual’s hesitancy across diverse time periods. It is acknowledged that a single word could potentially convey various meanings to different decision makers, such differences can be proficiently managed by utilizing personalized individual semantic (PIS) models. Previous approaches for calculating PIS failed to incorporate the individual’s updating preference information over time, which increases the risk that the computation of PIS is affected by random factors in a specific moment. In our current research, individual linguistic preference gathered over a time period are leveraged to form the PHFLTS. Additionally, a consistency driven optimization model based on PHFLTS is formulated to obtain PIS of linguistic terms. Subsequently, a fuzzy representation model termed as the fuzzy envelope of PHFLTS is introduced to facilitate the computation with words processes, integrating PHFLTS in LGDM. The practicality and legitimacy of these proposed models are evaluated through a comparative analysis. Lastly, these proposed models are tested and applied in a dedicated case study to further prove their usefulness and efficacy.

Interacting T-S fuzzy semantic model estimation for maneuvering target tracking

This paper proposes an interacting Takagi–Sugeno(T-S) fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, and constructs a framework for a generic interacting T-S fuzzy semantic model to incorporate semantic information concerning the target. To adaptively calculate the transition probability matrix of the models, a probabilistic switching model based on semantic fuzzy sets is derived by using the degree of intersection between fuzzy sets, which enables switching from one semantic fuzzy set to another. We also propose an efficient kernel maximum entropy fuzzy clustering method to identify the premise parameters of the model. This enables the proposed algorithm to recursively estimate the premise parameters in case of a limited number of samples. Moreover, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is used to identify the parameters of the T-S fuzzy semantic model. The results of experiments on three simulation datasets show that the proposed ITS-FSM algorithm is efficient, and is excellent at handling non-Gaussian noise.

Concept Solution Generation for Product Innovation under User Fuzzy Semantic Requirements

According to the classification of user requirements in Kano model, theory of technology evolution was adopted to determine the future evolution route of product and predict possible structure state for the future technolo- gy, in order to mine the user's potential requirements. In view of the fuzzy and uncertainty of users' potential requir- ements, the fuzzy semantic model was adopted to describe the qualitative user requirements in order to standardly form the user requirements concept sets with functional attributes in different requirement categories, so as to guide the designers to create the innovative solutions along the right direction and then achieve the product rapid innovatio- n design. Taking the Pulse-Jet bag filter as an example, the case analysis shows that this method can effectively be applied to obtain new product innovation concept solution, and provide theoretical support for the mapping between subsequent innovation concept solutions and innovation function solutions and innovation structure solutions, and also provide the model architecture for innovation droved new product digital prototype design.

Chinese Text Sentiment Analysis Utilizing Emotion Degree Lexicon and Fuzzy Semantic Model

Text on the web has become a valuable source for mining and analyzing user opinions on any topic. Non-native English speakers heavily support the growing use of Network media especially in Chinese. Many sentiment analysis studies have shown that a polarity lexicon can effectively improve the classification consequences. Social media, where users spontaneously generated content have become important materials for tracking people's opinions and sentiments. Meanwhile, the mathematical models of fuzzy semantics have provided a formal explanation for the fuzzy nature of human language processing. This paper investigated the limitations of traditional sentiment analysis approaches and proposed an effective Chinese sentiment analysis approach based on emotion degree lexicon. Inspired by various social cognitive theories, basic emotion value lexicon and social evidence lexicon were combined to improve sentiment analysis consequences. By using the composite lexicon and fuzzy semantic model, this new sentiment analysis approach obtains significant improvement in Chinese text.

Database design and querying within the fuzzy semantic model

Fuzzy semantic model (FSM) is a data model that uses basic concepts of semantic modeling and supports handling fuzziness, uncertainty and imprecision of real-world at the attribute, entity and class levels. The paper presents the principles and constructs of the FSM. It proposes ways to define the membership functions within all the constructs of the FSM. In addition, it provides a proposal for specifying FSM schema and introduce a query language adapted to FSM-based databases.

A Fuzzy Semantic Approach to Retrieving Bird Information Using Handheld Devices

We propose a new information retrieval model for handheld devices that is suitable for educational and entertainment activities. Our bird-searching system, with versions for PDAs and Tablet PCs, demonstrates the model's effectiveness. Our vision isn't simply to enhance learning with portability but rather to explore the new and varied educational activities that become available with our proposed fuzzy semantic model.

New directions in the analysis and interactive elicitation of personal construct systems

The computer elicitation and analysis of personal construct systems has become a technique of great interest and wide application in recent years. This paper takes the current state of the art as a starting point and explores further developments that are natural extensions of it. The overall objective of the work described is to develop man-computer symbiotic systems in which the computer is a truly dialectical partner to the person in forming theories and making decisions. A logical model of constructs as predicates applying to elements is used to develop a logical analysis of construct structures and this is contrasted with various distance-based clustering techniques. A grid analysis program called ENTAIL is described based on these techniques which derives a network of entailments from a grid. This is compared and contrasted with various programs for repertory grid analysis such as INGRID, FOCUS and Q-Analysis. Entailment is discussed in relation to Kelly's superordination hierarchy over constructs and preference relations over elements. The entailment analysis is extended to rating-scale data using a fuzzy semantic model. The significance of Kelly's notion of the opposite to a construct as opposed to its negation is discussed and related to other epistemological models and the role of relevance. Finally, the interactive construct elicitation program PEGASUS is considered in terms of the psychological and philosophical importance of the dialectical processes of grid elicitation and analysis, and recommendations are made about its generalization and extension based on the logical foundations described. Links are established between the work on repertory grids and that on relational data bases and expert systems.