By Mohamed A. Sharaf, Muhammad Aamir Cheema, Jianzhong Qi
This publication constitutes the refereed court cases of the twenty sixth Australasian Database convention, ADC 2015, held in Melbourne, VIC, Australia, in June 2015. The 24 complete papers offered including five demo papers have been conscientiously reviewed and chosen from forty three submissions. The Australasian Database convention is an annual overseas discussion board for sharing the newest learn developments and novel purposes of database platforms, facts pushed purposes and information analytics among researchers and practitioners from all over the world, relatively Australia and New Zealand. The undertaking of ADC is to percentage novel study ideas to difficulties of today’s details society that satisfy the desires of heterogeneous purposes and environments and to spot new concerns and instructions for destiny examine. ADC seeks papers from academia and proposing study on all useful and theoretical facets of complicated database thought and purposes, in addition to case reports and implementation experiences.
Read or Download Databases Theory and Applications: 26th Australasian Database Conference, ADC 2015, Melbourne, VIC, Australia, June 4-7, 2015. Proceedings PDF
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Extra info for Databases Theory and Applications: 26th Australasian Database Conference, ADC 2015, Melbourne, VIC, Australia, June 4-7, 2015. Proceedings
We then apply these attribute extractions as seeds to induce extractions from the open Web (Web texts and DOM trees). While Web texts extraction has been widely studied, we focus on attribute extraction from DOM trees. To the best of our knowledge, this is the ﬁrst approach to extracting attributes from DOM trees using open information extraction techniques. Experimental results show that our system achieves more comprehensive yet still accurate ontology augmentation. Our future work will focus on the enhancement of attribute description and the further improvement for the DOM tree extraction.
Finally, the intra-site feature ﬁlter may incorrectly take some Web sitespeciﬁc terms as attributes. For example, “edit” appears frequently in IMDb (a famous movie Website), but seldom contained by other Web sites. We remove such terms by examining the inter-site frequency feature of each attribute. Based on above discussion, we can obtain the credible attribute set by keeping only the attributes that appear evenly and frequently in many diﬀerent websites. Specifically, we calculate the inter-site frequency of an attribute, and use a predeﬁned threshold γ to exclude the attributes with low inter-site frequency.
2 Extracting Attributes from DOM Trees Extracting attributes from DOM trees is not completely new. Early supervised approaches [1,13] use manually deﬁned wrappers to extract attributes from each Website, which are time-consuming and non-scalable. ) can help reduce human intervention, but additionally requires labeled data for the training, and are inapplicable to new websites that have not been handled before. Generative models designed in  alleviate this problem by segmenting and labeling the training samples, but they can only extract the attributes that are predeﬁned in the training data.
Databases Theory and Applications: 26th Australasian Database Conference, ADC 2015, Melbourne, VIC, Australia, June 4-7, 2015. Proceedings by Mohamed A. Sharaf, Muhammad Aamir Cheema, Jianzhong Qi