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We invite research contributions to the Web Mining and Content Analysis Track at the 31st edition of the Web Conference series (formerly known as WWW), to be held April 20-24, 2020 in Taipei (https://www2020.thewebconf.org/).The Web Mining and Content Analysis track welcomes submissions of original and high-quality research papers related to the extraction of information from the Web, the analysis of the Web text and social media content, recommendation, and mining of the Web usage data.Accepted papers will appear in the online conference proceedings published by the ACM Digital Library and the conference’s web site.
The multitude of rich information sources available on the Web today provides wonderful opportunities and challenges to Web mining for a diverse range of applications: constructing large knowledge bases, predicting the future, finding trends and epidemic in a population, marketing and recommendation, as well as filtering and cleaning Web content to improve the experience of users consuming it.
This track covers data analysis for a wide variety of Web data including tweets, tags, links, logs, images, videos, and other multimodal data.
Web mining is evaluated by using data mining techniques, namely classification, clustering, and association rules.
It has some beneficial areas or applications such as Electronic commerce, E-learning, E-government, E-policies, E-democracy, Electronic business, security and crime investigation and digital library.
There is a vast amount of unstructured Arabic information on the Web, this data is always organized in semi-structured text and cannot be used directly.
This research proposes a semi-supervised technique that extracts binary relations...
A small set of a handful of instance relations are required as input from the user.
The system exploits summaries from Google search engine as a source text. The output is a set of new entities and their relations.
We especially encourage submissions that propose novel and principled techniques or algorithms that can leverage the special characteristics of the Web, its social media, and user behaviors for such extraction and mining.
In addition to new techniques and algorithms, we also seek insights gained from the mining process.