Learning to integrate web taxonomies

Learning to integrate web taxonomies

We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects ...

Learning to Integrate Web Taxonomies with Fine-Grained Relations: A Case Study Using Maximum Entropy Model

As web taxonomy integration is an emerging issue on the Internet, many research topics, such as personalization, web searches, and electronic markets, would benefit from further development of taxonomy integration techniques. The integration task is to transfer documents from a source web taxonomy to a target web taxonomy. In most current techniques, integration performance is enhanced by refer...

Multi-faceted Learning for Web Taxonomies

A standard problem for internet commerce is the task of building a product taxonomy from web pages, without access to corporate databases. However, a nasty aspect of the real world is that most web-pages have multiple facets. A web page might contain information about both cameras and computers, as well as having both specification and sale data. We are interested in methods for supervised and ...

Multi-fa eted Learning for Web Taxonomies

Effective Learning to Rank Persian Web Content

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a ...