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A machine learning approach to link prediction for interlinked documents

Journal Article


Abstract


  • This paper provides an explanation to how a recently developed machine learning approach, namely the Probability Measure Graph Self-Organizing Map (PM-GraphSOM) can be used for the generation of links between referenced or otherwise interlinked documents. This new generation of SOM models are capable of projecting generic graph structured data onto a fixed sized display space. Such a mechanism is normally used for dimension reduction, visualization, or clustering purposes. This paper shows that the PM-GraphSOM training algorithm ``inadvertently'' encodes relations that exist between the atomic elements in a graph. If the nodes in the graph represent documents, and the links in the graph represent the

    reference (or hyperlink) structure of the documents, then it is possible to obtain a set of links for a test document whose link structure is unknown. A significant finding of this paper is that the described approach is scalable in that links can be extracted in linear time. It will also be shown that the proposed approach is capable of predicting the pages which would be linked to a new document, and is capable of predicting thelinks to other documents from a given test document. The approach is applied to web pages from Wikipedia, a relatively large XML text database consisting of many referenced documents.

UOW Authors


Publication Date


  • 2010

Citation


  • Kc, M., Chau, R., Hagenbuchner, M., Tsoi, A. & Lee, V. (2010). A machine learning approach to link prediction for interlinked documents. S. Geva, J. Kamps & A. Trotman In INEX 2009: 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, 7-9 Dec 2009, Brisbane, Australia. Lecture Notes in Computer Science, 6203 (2010), 342-354.

Scopus Eid


  • 2-s2.0-77955313484

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3322

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 342

End Page


  • 354

Volume


  • 6203

Issue


  • 2010

Place Of Publication


  • Germany

Abstract


  • This paper provides an explanation to how a recently developed machine learning approach, namely the Probability Measure Graph Self-Organizing Map (PM-GraphSOM) can be used for the generation of links between referenced or otherwise interlinked documents. This new generation of SOM models are capable of projecting generic graph structured data onto a fixed sized display space. Such a mechanism is normally used for dimension reduction, visualization, or clustering purposes. This paper shows that the PM-GraphSOM training algorithm ``inadvertently'' encodes relations that exist between the atomic elements in a graph. If the nodes in the graph represent documents, and the links in the graph represent the

    reference (or hyperlink) structure of the documents, then it is possible to obtain a set of links for a test document whose link structure is unknown. A significant finding of this paper is that the described approach is scalable in that links can be extracted in linear time. It will also be shown that the proposed approach is capable of predicting the pages which would be linked to a new document, and is capable of predicting thelinks to other documents from a given test document. The approach is applied to web pages from Wikipedia, a relatively large XML text database consisting of many referenced documents.

UOW Authors


Publication Date


  • 2010

Citation


  • Kc, M., Chau, R., Hagenbuchner, M., Tsoi, A. & Lee, V. (2010). A machine learning approach to link prediction for interlinked documents. S. Geva, J. Kamps & A. Trotman In INEX 2009: 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, 7-9 Dec 2009, Brisbane, Australia. Lecture Notes in Computer Science, 6203 (2010), 342-354.

Scopus Eid


  • 2-s2.0-77955313484

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3322

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 342

End Page


  • 354

Volume


  • 6203

Issue


  • 2010

Place Of Publication


  • Germany