Ontology learning algorithm for similarity measuring and ontology mapping using linear programming

2017 ◽  
Vol 33 (5) ◽  
pp. 3153-3163 ◽  
Author(s):  
Wei Gao ◽  
Linli Zhu ◽  
Yun Guo ◽  
Kaiyun Wang
Keyword(s):  
Linear Programming ◽  
Learning Algorithm ◽  
Ontology Mapping ◽  
Ontology Learning ◽  
Similarity Measuring

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

2015 ◽  
Vol 25 (14) ◽  
pp. 1540034 ◽  
Author(s):  
Wei Gao ◽  
Linli Zhu ◽  
Kaiyun Wang
Keyword(s):  
Physics Education ◽  
Learning Algorithm ◽  
Augmented Lagrangian Method ◽  
Ontology Mapping ◽  
Sparse Learning ◽  
Alternating Direction ◽  
Learning Techniques ◽  
Sparse Vector ◽  
Plant Ontology ◽  
Similarity Measuring

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

scholarly journals Ontology optimization tactics via distance calculating

2016 ◽  
Vol 1 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Yun Gao ◽  
Mohammad Reza Farahani ◽  
Wei Gao
Keyword(s):  
Similarity Measure ◽  
Distance Function ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Ontology Mapping ◽  
Ontology Learning ◽  
Real Numbers ◽  
Function Learning ◽  
Engineering Applications ◽  
Learning Techniques

AbstractIn this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning techniques. Using the distance computation formulation, all the pairs of ontology vertices are mapped into real numbers which express the distance of their corresponding vectors. The more distance between two vertices, the smaller similarity between their corresponding concepts. The stabilities of our learning algorithm are defined and several bounds are yielded via stability assumptions. The simulation experimental conclusions show that the new proposed ontology algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in certain engineering applications.

Partial multi-dividing ontology learning algorithm

2018 ◽  
Vol 467 ◽  
pp. 35-58 ◽  
Author(s):  
Wei Gao ◽  
Juan L.G. Guirao ◽  
B. Basavanagoud ◽  
Jianzhang Wu
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning

Primal dual based ontology sparse vector learning for similarity measuring and ontology mapping

2018 ◽  
Vol 35 (4) ◽  
pp. 4525-4531
Author(s):  
Shu Gong ◽  
Liwei Tian ◽  
Muhammad Imran ◽  
Wei Gao
Keyword(s):  
Ontology Mapping ◽  
Sparse Vector ◽  
Primal Dual ◽  
Similarity Measuring

scholarly journals Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds

2021 ◽  
Author(s):  
Xiaocheng Li ◽  
Yinyu Ye
Keyword(s):  
Linear Programming ◽  
Learning Algorithm ◽  
Equivalent Form ◽  
Sample Average Approximation ◽  
Smoothness Condition ◽  
Regularity Conditions ◽  
Linear Programs ◽  
Constraint Matrix ◽  
The Past ◽  
Dependent Learning

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown distribution and revealed sequentially over time. Virtually all existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LPs), and their analyses were focused on the aggregate objective value and solving the packing LP, where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions were as follows. (i) Does the set of LP optimal dual prices learned in the existing algorithms converge to those of the “offline” LP? (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative? We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Specifically, we identify an equivalent form of the dual problem that relates the dual LP with a sample average approximation to a stochastic program. Furthermore, we propose a new type of OLP algorithm, action-history-dependent learning algorithm, which improves the previous algorithm performances by taking into account the past input data and the past decisions/actions. We derive an [Formula: see text] regret bound (under a locally strong convexity and smoothness condition) for the proposed algorithm, against the [Formula: see text] bound for typical dual-price learning algorithms, where n is the number of decision variables. Numerical experiments demonstrate the effectiveness of the proposed algorithm and the action-history-dependent design.

Disequilibrium multi-dividing ontology learning algorithm

2017 ◽  
Vol 46 (18) ◽  
pp. 8925-8942 ◽  
Author(s):  
Jianzhang Wu ◽  
Xiao Yu ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning
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