Linked data triples enhance document relevance classification Nagumothu, Dinesh, Eklund, Peter, Ofoghi, Bahadorreza and Bouadjenek, Mohamed Reda 2021, Linked data triples enhance document relevance classification, Applied sciences, vol. 11, no. 14, Special Issue: Advances in Artificial Intelligence: Machine Learning, Data Mining and Data Sciences, pp. 1-21, doi: 10.3390/app11146636. Attached Files Name Description MIMEType Size Downloads Title Linked data triples enhance document relevance classification Author(s) Nagumothu, Dinesh Eklund, Peter orcid.org/0000-0003-2313-8603 Ofoghi, Bahadorreza orcid.org/0000-0003-0579-8018 Bouadjenek, Mohamed Reda orcid.org/0000-0003-1807-430X Journal name Applied sciences Volume number 11 Issue number 14 Season Special Issue: Advances in Artificial Intelligence: Machine Learning, Data Mining and Data Sciences Article ID 6636 Start page 1 End page 21 Total pages 21 Publisher MDPI AG Place of publication Basel, Switzerland Publication date 2021 ISSN 2076-3417 Summary Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets. Language eng DOI 10.3390/app11146636 Indigenous content off HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30153700 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 20 Jul 2021, 22:21:44 EST Wed, 21 Jul 2021, 05:00:55 EST Wed, 21 Jul 2021, 06:04:48 EST Wed, 21 Jul 2021, 13:49:06 EST Fri, 23 Jul 2021, 06:06:58 EST Tue, 03 Aug 2021, 09:55:08 EST Tue, 03 Aug 2021, 10:01:46 EST Tue, 03 Aug 2021, 10:02:26 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 20 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 20 Jul 2021, 22:21:44 EST

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