Approximation analysis of ontology learning algorithm in linear combination setting

Gao, Wei; Chen, Yaojun

doi:10.1186/s13677-020-00173-y

Cited by 8 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML techniques [4][5][6] have developed rapidly in recent years and have yielded very good results in classification and regression tasks for solving many complex problems. These technologies have shown great potential in various engineering fields, such as civil engineering, [7][8][9] structural health monitoring, [10][11][12] and transportation engineering.…”

mentioning

confidence: 99%

An improved random forest algorithm and its application to wind pressure prediction

Lang

Tiancai

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over‐fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high‐rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high‐rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large‐span building type wind tunnel tests.

show abstract

mentioning

confidence: 99%

An improved random forest algorithm and its application to wind pressure prediction

Lang

Tiancai

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…Apart from activities and techniques for ontology management, METHONTOLOGY summarizes separate steps for the developement of an ontology, like “ […] the specification, the conceptualization, the formalization, the implementation and the maintenance […] ” [ 15 ]. A theoretical perspective on ontologies was also considered [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

Steiner

Saalfeld

Elgert

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Adherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval. Methods First, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology “Ontology for factors influencing therapy adherence to rehabilitation” (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts. Results OnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area. Conclusions With OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

show abstract

“…Hence, the entire ontology is represented by a graph. In addition, after all the information related to the concept is numerically expressed, a multi-dimensional vector is used to encapsulate the representation, that is, each vertex is a fixed p-dimensional vector, and then a learning model can be used to learn various ontology graphs [15].…”

mentioning

confidence: 99%

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Zhu

Hua

2020

IEEE Access

View full text Add to dashboard Cite

The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of U-statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications. INDEX TERMS small ontology, multi-dividing ontology learning, similarity measure I. INTRODUCE OF ONTOLOGY

show abstract

Approximation analysis of ontology learning algorithm in linear combination setting

Cited by 8 publications

References 38 publications

An improved random forest algorithm and its application to wind pressure prediction

An improved random forest algorithm and its application to wind pressure prediction

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Contact Info

Product

Resources

About