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Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach

Dobbins, C and Fergus, P (2015) Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach. In: Neural Networks (IJCNN), 2015 International Joint Conference on . pp. 1-8. (International Joint Conference on Neural Networks (IJCNN), 12 July 2015 - 17 July 2015, Killarney, Ireland).

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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 internet-enabled 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Linked Data; RDF; Matrix; Vector; Machine Learning; Artificial Neural Network; Semantic Web
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 16 Oct 2015 10:03
Last Modified: 13 Apr 2022 15:13
URI: https://researchonline.ljmu.ac.uk/id/eprint/1179

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