Practice of Inductive Reasoning on the Semantic Web: A System for Semantic Web Mining

2006 ◽  
pp. 242-256 ◽  
Author(s):  
Francesca A. Lisi
Keyword(s):  
Semantic Web ◽  
Web Mining ◽  
Inductive Reasoning

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

scholarly journals Semantic Web Mining using RDF Data

2016 ◽  
Vol 133 (10) ◽  
pp. 14-19 ◽  
Author(s):  
V. A. ◽  
Amruta A.
Keyword(s):  
Semantic Web ◽  
Web Mining ◽  
Rdf Data

scholarly journals A State-of-the-Art Survey on Semantic Web Mining

2013 ◽  
Vol 05 (01) ◽  
pp. 10-17 ◽  
Author(s):  
Qudamah K. Quboa ◽  
Mohamad Saraee
Keyword(s):  
Semantic Web ◽  
Web Mining ◽  
State Of The Art

Extracting significant Website Key Objects: A Semantic Web mining approach

2011 ◽  
Vol 24 (8) ◽  
pp. 1532-1541 ◽  
Author(s):  
Juan D. Velásquez ◽  
Luis E. Dujovne ◽  
Gaston L’Huillier
Keyword(s):  
Semantic Web ◽  
Web Mining

Semantic Web Search and Inductive Reasoning

2013 ◽  
pp. 237-261 ◽  
Author(s):  
Claudia d’Amato ◽  
Nicola Fanizzi ◽  
Bettina Fazzinga ◽  
Georg Gottlob ◽  
Thomas Lukasiewicz
Keyword(s):  
Semantic Web ◽  
Web Search ◽  
Inductive Reasoning

Analyzing changes in oncology trial records over time using automated semantic web-mining.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e13073-e13073
Author(s):  
David J Cocker ◽  
Johanna Caroline Lievens ◽  
Kristof Geentjens ◽  
Piet Van Remortel ◽  
Mireille De Cre ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Semantic Web ◽  
Language Processing ◽  
Web Mining ◽  
Semantic Analysis ◽  
Phase Iii ◽  
Differential Analysis ◽  
Clinical Trial Registries ◽  
The Uk ◽  
Over Time

e13073 Background: Incorrect assumptions and poor feasibility are often cited as the major cause of study delay and outcome resolution. Trial placement and recruitment can be optimized by monitoring and regular assessment of trial registries. However, just a snapshot analysis does not take into account retrospective editing of records. This paper evaluates the effectiveness of a robotic, semantic analysis of registered oncology trials to assess individual changes in trial records over time. Methods: Evaluating individual trial records from 2008 to 2012 with state-of-the art natural language processing and semantic linking of data fields within clinical trial registries and literature databases to add increased resolution to erroneous or inadequate registry fields. Results: Substantial modifications are made to registration records over the life cycle of a clinical trial. Quantifiable fields as recruitment, site numbers and end dates all deviate significantly from the study roll-out. Analysis of oncology trials recruiting over 200 patients between 2008 and 2012 shows two thirds of trials deviate from the initial record. Most frequently study end date is delayed (about 50% of all trials) or enrollment targets are amended (at least 25%). Over all oncology indications, enrollment increases are twice more frequent as decreases. However, there are notable exceptions such as lung cancer, where enrollment is frequently decreased. A differential analysis of countries and sites in commercial oncology phase III trials over the period revealed a marked shift of research to sites in France, Spain, the UK and China. However, when sites were added to ongoing trials this general trend was not entirely followed as Poland, Japan, Belgium, Argentina and Switzerland were frequently chosen as additional countries above the normal distribution, suggesting these are preferred countries for trial “rescue”. Conclusions: This paper demonstrates the effectiveness of automated semantic web-mining to identify incorrect clinical trial assumptions and subsequent remediation.

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