A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy

Abstract

Constructing a merged concept lattice with formal concept analysis (FCA) is an important research direction in the field of integrating multi-source geo-ontologies. Extracting essential geographical properties and reducing the concept lattice are two key points of previous research. A formal integration method is proposed to address the challenges in these two areas. We first extract essential properties from multi-source geo-ontologies and use FCA to build a merged formal context. Second, the combined importance weight of each single attribute of the formal context is calculated by introducing the inclusion degree importance from rough set theory and information entropy; then a weighted formal context is built from the merged formal context. Third, a combined weighted concept lattice is established from the weighted formal context with FCA and the importance weight value of every concept is defined as the sum of weight of attributes belonging to the concept's intent. Finally, semantic granularity of concept is defined by its importance weight; we, then gradually reduce the weighted concept lattice by setting up diminishing threshold of semantic granularity. Additionally, all of those reduced lattices are organized into a regular hierarchy structure based on the threshold of semantic granularity. A workflow is designed to demonstrate this procedure. A case study is conducted to show feasibility and validity of this method and the procedure to integrate multi-source geo-ontologies.

[Retracted] Rough Set Construction and Entropy Weight Evaluation of Urban Higher Education Resource Carrying Capacity Based on Big Data

“…e entropy weight method is an objective method for determining index weight introduced by information theory. e basic idea is to calculate the entropy weight of an index according to its variability, which is inversely proportional to the variation degree of the index value, the amount of information provided, and its role in comprehensive evaluation [21]. Compared with the qualitative index weight assignment method, the entropy weight method has stronger objectivity and higher accuracy and can better explain the calculated results.…”

Entropy-Based Method to Evaluate Contact-Pressure Distribution for Assembly-Accuracy Stability Prediction

“…The maximum entropy principle is extensively applied in many disciplines [ 28 , 29 , 30 , 31 , 32 ]. In information theory, if there is a discrete random variable, X , with possible values of { x1, x2, …, xn }, and probability mass function, P(X ), the entropy, H, of X is defined as follows: where E is the expectation operator, r is the logarithmic base, which generally takes a value of two [ 33 , 34 , 35 ] (in this study, r = 2). When the probability of each random variable, x i , is the same, i.e., p 1 = p 2 =... = p n = , the maximum value of the entropy function, H (X) , is obtained, and the corresponding maximum value can be calculated as…”

Entropy-Based Method for Evaluating Contact Strain-Energy Distribution for Assembly Accuracy Prediction

“…In terms of an event, x i , it has a probability, p i , i.e., p i = P(X = x i ); r is the base of the logarithm, which usually takes r = 2 [28][29][30][31][32][33] (In this study, we take r = 2). The entropy H(X) possesses a maximum property, which implies that the measure will be maximal if all the outcomes are equally likely.…”