Gradient Learning Algorithms for Ontology Computing

Gao, Wei; Zhu, Linli

doi:10.1155/2014/438291

Cited by 30 publications

(7 citation statements)

References 16 publications

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“…Humanoid robotics ontologies (denoted by O 5 and O 6 , constructed by Gao and Zhu [12], and the structures of O 5 and O 6 can refer to in Fig. 5 and Fig.…”

Section: Ontology Mapping Experiments On Humanoid Robotics Datamentioning

confidence: 99%

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Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Zhu

Pan

Wang

2017

Applied Mathematics and Nonlinear Sciences

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In information science and other engineering applications, ontology plays an irreplaceable role to find the intrinsic semantic link between concepts and to determine the similarity score returned to the user. Ontology mapping aims to excavate the intrinsic semantic relationship between concepts from different ontologies, and the essence of these applications is similarity computation. In this article, we propose the new ontology sparse vector approximation algorithms based on the affine transformation tricks. By means of these techniques, we study the equivalent form of ontology dual problem and determine its feasible set. The simulation experiments imply that our new proposed ontology algorithm has high efficiency and accuracy in ontology similarity computation and ontology mapping in biology, chemical and related fields.

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“…Humanoid robotics ontologies (denoted by O 5 and O 6 , constructed by Gao and Zhu [12], and the structures of O 5 and O 6 can refer to in Fig. 5 and Fig.…”

Section: Ontology Mapping Experiments On Humanoid Robotics Datamentioning

confidence: 99%

“…There are several effective learning tricks in ontology similarity measure and ontology mapping. Gao and Zhu [12] studied the gradient learning algorithms for ontology similarity computing and ontology mapping. Gao and Xu [13] obtained the stability analysis for ontology learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Zhu

Pan

Wang

2017

Applied Mathematics and Nonlinear Sciences

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“…Dialogue acts have been used to represent key aspects in dialogue systems, from corpus annotation to the design of dialogue models or the representation of the behavior of interlocutors during the conversation. The semantic representation of information can also be achieved through other models such as Hybrid Deep Belief Networks (HDBNs) [ 21 ], time-dependent semantic similarities [ 22 ], fuzzy set theories [ 20 ], or ontologies [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

Griol

Callejas

2016

Computational Intelligence and Neuroscience

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Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.

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“…We use humanoid robotics ontologies O 5 and O 6 (constructed by Gao and Zhu [13], and the structures of which are deduced by Algorithm 1. P@N criterion is applied to measure the equality of the experiment.…”

Section: Experiments On Humanoid Robotics Datamentioning

confidence: 99%

New Multi-dividing Ontology Learning Algorithm Using Special Loss Functions

Gao¹,

Li²,

Xu³

2015

TOCSJ

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Abstract:As an important data structure model, ontology has become one of the core contents in information science. Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency. In this paper, in terms of multi-dividing proper loss functions, we propose new multi-dividing ontology learning algorithms for similarity measure and ontology mapping construction. Several theoretical statistical characteristics supporting the new learning model are given. Finally, four experiments on different scientific fields verify that our multi-dividing ontology algorithm has high accuracy and efficiency in application implements.

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Gradient Learning Algorithms for Ontology Computing

Cited by 30 publications

References 16 publications

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

New Multi-dividing Ontology Learning Algorithm Using Special Loss Functions

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