Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

Rubiolo, María Florencia; Caliusco, María Laura; Stegmayer, Georgina; Coronel, Mauricio; Fabrizi, Marco

doi:10.1016/j.ins.2011.08.008

Cited by 36 publications

(13 citation statements)

References 16 publications

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“…However, they are often over trained as they adopt a trial-and-error approach to seek possible values of parameters for convergence of the global optimum [24]. Nevertheless, feedforward approaches have been used to match ontology models on the Semantic Web [26]. In this case, the results provided a modest average accuracy between 77 -79%.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Dobbins

Fergus

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internetenabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of 'understanding' the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively.

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Section: B Artificial Neural Networkmentioning

confidence: 99%

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Dobbins

Fergus

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…In addition, there are many studies about using machine learning to achieve ontology mapping. In [22], [23] neural network was adopted to train the weights of individual similarities, which requires a lot of samples, and the network structure was set by the researchers subjectively, and this method may lead to not have a global optimal solution. In R. Ichise [24] SVM was applied to classify similarity vectors to achieve ontology mapping without using the weights.…”

Section: Related Workmentioning

confidence: 99%

A Similarity-Based Concepts Mapping Method between Ontologies

Liu

Qin

Gao

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYOntology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

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“…Rowe et al [50] introduced a behavior ontology that captured the user behavior within a given context and inferred the role of a user by using semantic-rule based methodology. An Ontology of Information Security is developed by Herzog et al [28], which describes assets, threats, vulnerabilities, countermeasures and their relations.…”

Section: Ontology Based Intrusion Detection Systems (Oids)mentioning

confidence: 99%

An Imperative Analysis of diverse State of Art Solutions for Internet and Web Application Security

2014

International Conference on Computer Science and Information Systems (ICSIS’2014) Oct 17-18, 2014 Dubai (UAE)

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Internet hold the potential to transform the setting of services and information by making more attractive and creative which enhanced the productivity of e-business and communication. For the recent years, internet and web applications are gaining an innovative momentum as a computing paradigm for providing flexible and dynamic services on demand. On the other hand, the number of security vulnerabilities and dangerous security flaw has increased immensely due to its growing popularity and global distribution. To tackle these risks, networks require increased security protection. It is important to maintain a high level security to ensure safe and trusted communication of information between various organizations Internet application security protects the confidentiality, integrity and availability of resources in cyberspace. This paper gives an imperative analysis in the area of internet and web application security, the characteristics of some of the best known methods of art solutions for internet application security and their flaws

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Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

Cited by 36 publications

References 16 publications

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

A Similarity-Based Concepts Mapping Method between Ontologies

An Imperative Analysis of diverse State of Art Solutions for Internet and Web Application Security

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